Nonverbal information generation apparatus, nonverbal information generation model learning apparatus, methods, and programs

ABSTRACT

A nonverbal information generation apparatus includes a nonverbal information generation unit that generates time-information-stamped nonverbal information that corresponds to time-information-stamped text feature quantities and an expression unit that expresses the nonverbal information on the basis of the time-information-stamped text feature quantities and a learned nonverbal information generation model. The time-information-stamped text feature quantities are configured to include feature quantities that have been extracted from text and time information representing times assigned to predetermined units of the text. The nonverbal information is information for controlling the expression unit so as to express behavior corresponding to the text. The nonverbal information generation unit controls the expression unit so that the time-information-stamped nonverbal information is expressed from the expression unit in accordance with time information assigned to the nonverbal information, and controls the expression unit so that the text or voice corresponding to the text that corresponds to the time information is output.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a 371 National Stage of International ApplicationNo. PCT/JP2019/005658, filed on Feb. 15, 2019, which claims priority toJapanese Patent Application No. 2018-026516, filed Feb. 16, 2018,Japanese Patent Application No. 2018-026517, filed Feb. 16, 2018,Japanese Patent Application No. 2018-097338, filed May 21, 2018,Japanese Patent Application No. 2018-097339, filed May 21, 2018, andJapanese Patent Application No. 2018-230311 filed, Dec. 7, 2018. Theentire disclosures of the above applications are incorporated herein byreference.

TECHNICAL FIELD

The present invention relates to a nonverbal information generationapparatus, a nonverbal information generation model learning apparatus,methods, and programs.

BACKGROUND ART

In communication, in addition to verbal behavior, nonverbal behavior hasan important function in transmitting emotions and intentions.Therefore, it is desired that communication robots and communicationagents also exhibit nonverbal behavior in order to communicate smoothlywith users. From such a background, a technique has been proposed inwhich a nonverbal action corresponding to an utterance is registered ina database (DB) in advance, and the nonverbal action is expressed inaccordance with the reproduction of the utterance (for example, refer toPatent Document 1).

PRIOR ART DOCUMENT Patent Document

Patent Document 1: Japanese Unexamined Patent Application FirstPublication No. 2003-173452

SUMMARY OF INVENTION Problems to be Solved by the Invention

However, in the technique described in Patent Document 1, it isnecessary to create and register in advance information on utterancesand nonverbal actions (action commands composed of a set of gestures andspoken lines), and thus the cost of data creation is high.

The present invention has been made in view of the above circumstances,and has as its object to provide a nonverbal information generationapparatus, a nonverbal information generation model learning apparatus,methods, and programs capable of automating the association of at leastone of voice information and text information with nonverbalinformation.

Means for Solving the Problems

In order to achieve the abovementioned object, a nonverbal informationgeneration apparatus according to a first aspect is a nonverbalinformation generation apparatus that includes: a nonverbal informationgeneration unit that generates time-information-stamped nonverbalinformation that corresponds to time-information-stamped text featurequantities on the basis of the time-information-stamped text featurequantities and a learned nonverbal information generation model; and anexpression unit that expresses the nonverbal information, and thetime-information-stamped text feature quantities are configured tocomprise feature quantities that have been extracted from text and timeinformation representing times assigned to predetermined units of thetext, the nonverbal information is information for controlling theexpression unit so as to express behavior that corresponds to the text,and the nonverbal information generation unit controls the expressionunit so that the time-information-stamped nonverbal information isexpressed from the expression unit in accordance with time informationassigned to the nonverbal information, and controls the expression unitso that the text or voice corresponding to the text that corresponds tothe time information is output. The time information may be assigned onthe basis of a result of partitioning a range of time when the text isoutput in accordance with the number of partitions when the text hasbeen partitioned in the predetermined units.

The nonverbal information generation unit may generate thetime-information-stamped nonverbal information that satisfies aconstraint condition relating to time series of the nonverbalinformation or a constraint condition relating to the nonverbalinformation corresponding to the same predetermined units.

The nonverbal information generation apparatus may further include acontrol unit that changes the time information representing the timesassigned to the predetermined units of the text or the time informationof the time-information-stamped nonverbal information so as to satisfythe constraint condition relating to the time series of the nonverbalinformation or the constraint condition relating to the nonverbalinformation corresponding to the same predetermined units.

A nonverbal information generation model learning apparatus according toa second aspect is configured to include: a learning informationacquisition unit that acquires text information representing text; alearning feature quantity extraction unit that extracts, from the textinformation acquired by the learning information acquisition unit,time-information-stamped text feature quantities that are configured tocomprise feature quantities extracted from the text and time informationrepresenting times assigned to predetermined units of the text; anonverbal information acquisition unit that acquires nonverbalinformation representing information relating to behavior of a speakeror behavior of a listener of speaking of the speaker corresponding tothe text when the speaker performed the speaking, and time informationrepresenting times at which the behavior was performed and correspondingto the nonverbal information, and creates time-information-stampednonverbal information; and a learning unit that learns a nonverbalinformation generation model for generating the time-information-stampednonverbal information acquired by the nonverbal information acquisitionunit on the basis of the time-information-stamped text featurequantities extracted by the learning feature quantity extraction unit.

Moreover, according to a nonverbal information generation methodaccording to a third aspect, a nonverbal information generation unitgenerates time-information-stamped nonverbal information thatcorresponds to time-information-stamped text feature quantities on thebasis of the time-information-stamped text feature quantities and alearned nonverbal information generation model, and an expression unitexpresses the nonverbal information, and the time-information-stampedtext feature quantities are configured to comprise feature quantitiesthat have been extracted from text and time information representingtimes assigned to predetermined units of the text, the nonverbalinformation is information for controlling the expression unit so as toexpress behavior that corresponds to the text, and the nonverbalinformation generation unit controls the expression unit so that thetime-information-stamped nonverbal information is expressed from theexpression unit in accordance with time information assigned to thenonverbal information, and controls the expression unit so that the textor voice corresponding to the text that corresponds to the timeinformation is output.

Moreover, according to a nonverbal information generation model learningmethod according to a fourth aspect, a learning information acquisitionunit acquires text information representing text, a learning featurequantity extraction unit extracts, from the text information acquired bythe learning information acquisition unit, time-information-stamped textfeature quantities that are configured to comprise feature quantitiesextracted from the text and time information representing times assignedto predetermined units of the text, a nonverbal information acquisitionunit acquires nonverbal information representing information relating tobehavior of a speaker or behavior of a listener of speaking of thespeaker corresponding to the text when the speaker performed thespeaking, and time information representing times at which the behaviorwas performed and corresponding to the nonverbal information, andcreates time-information-stamped nonverbal information, and a learningunit learns a nonverbal information generation model for generating thetime-information-stamped nonverbal information acquired by the nonverbalinformation acquisition unit on the basis of thetime-information-stamped text feature quantities extracted by thelearning feature quantity extraction unit.

Moreover, a program according to a fifth aspect is a program for causinga computer to function as each unit included in the nonverbalinformation generation apparatuses.

Advantageous Effects of the Invention

As described above, the nonverbal information generation apparatus,nonverbal information generation model learning apparatus, methods, andprograms according to the present invention can automate the associationof at least one of voice information and text information with nonverbalinformation.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing an example of the configuration of anonverbal information generation model learning apparatus in accordancewith a first embodiment.

FIG. 2 is an explanatory diagram for describing a method of acquiringlearning data.

FIG. 3 is an explanatory diagram for describing voice feature quantitiesand text feature quantities used in the present embodiment.

FIG. 4 is a block diagram showing an example of the configuration of anonverbal information generation apparatus in accordance with the firstembodiment.

FIG. 5 is a flowchart showing an example of the flow of the learningprocess in accordance with the first embodiment.

FIG. 6 is a flowchart showing an example of the flow of the nonverbalinformation generation process in accordance with the first embodiment.

FIG. 7 is a block diagram illustrating an example of the configurationof the nonverbal information generation model learning apparatus inaccordance with a second embodiment.

FIG. 8 is a diagram illustrating a detailed configuration example of alearning information acquisition unit in accordance with the secondembodiment.

FIG. 9 is a block diagram showing an example of the configuration of anonverbal information generation apparatus in accordance with the secondembodiment.

FIG. 10 is a diagram showing a detailed configuration example of aninformation acquisition unit in accordance with the second embodiment.

FIG. 11 is a flowchart showing an example of the flow of the learningprocess in accordance with the second embodiment.

FIG. 12 is a flowchart showing an example of the flow of the nonverbalinformation generation process in accordance with the second embodiment.

FIG. 13A is an explanatory diagram for describing the learninginformation acquisition unit and a learning feature quantity extractionunit in accordance with the third embodiment.

FIG. 13B is an explanatory diagram for describing an informationacquisition unit and a feature quantity extraction unit in accordancewith the third embodiment.

FIG. 14A is an explanatory diagram for describing a modified example ofthe third embodiment.

FIG. 14B is an explanatory diagram for describing a modified example ofthe third embodiment.

FIG. 15 is an explanatory diagram for describing a combination ofconfigurations of other embodiments.

FIG. 16 is an explanatory diagram for describing a combination ofconfigurations of other embodiments.

FIG. 17 is a block diagram showing an example of the configuration ofthe nonverbal information generation model learning apparatus inaccordance with a seventh embodiment.

FIG. 18 is an explanatory diagram for describing additional informationused in the present embodiment.

FIG. 19 is a block diagram showing an example of the configuration ofthe nonverbal information generation apparatus in accordance with theseventh embodiment.

FIG. 20 is a flowchart illustrating an example of the flow of thelearning process in accordance with the seventh embodiment.

FIG. 21 is a flowchart illustrating an example of the flow of thenonverbal information generation process in accordance with the seventhembodiment.

FIG. 22 is a block diagram showing an example of the configuration ofthe nonverbal information generation model learning apparatus inaccordance with another example of the seventh embodiment.

FIG. 23 is a block diagram showing an example of the configuration ofthe nonverbal information generation apparatus in accordance withanother example of the seventh embodiment.

FIG. 24 is a diagram for describing a method of changing timeinformation using additional information.

FIG. 25 is a block diagram showing an example of the configuration ofthe nonverbal information generation model learning apparatus inaccordance with an eighth embodiment.

FIG. 26A is a diagram for describing a method of assigning detailed timeinformation.

FIG. 26B is a block diagram showing an example of the configuration ofthe nonverbal information generation apparatus in accordance with theeighth embodiment.

FIG. 27 is a flowchart showing an example of the flow of the learningprocess in accordance with the eighth embodiment.

FIG. 28 is a flowchart showing an example of the flow of the nonverbalinformation generation process in accordance with the eighth embodiment.

FIG. 29 is a flowchart showing an example of the flow of the learningprocess in accordance with a ninth embodiment.

FIG. 30 is a flowchart showing an example of the flow of the nonverbalinformation generation process in accordance with the ninth embodiment.

FIG. 31 is a block diagram showing an example of the configuration of anonverbal information generation apparatus in accordance with aneleventh embodiment.

FIG. 32 is a drawing showing an example of the display screen inaccordance with the eleventh embodiment.

FIG. 33 is a drawing for describing an instruction to change timeinformation.

FIG. 34 is a drawing for describing an instruction to change timeinformation.

FIG. 35 is a flowchart showing an example of the flow of the nonverbalinformation generation process in accordance with the eleventhembodiment.

FIG. 36 is a flowchart showing an example of the flow of the displaycontrol process in accordance with the eleventh embodiment.

MODES FOR CARRYING OUT THE INVENTION

Hereinbelow, detailed descriptions will be given for examples of modesfor carrying out the present invention with reference to the drawings.

Overview of Present Embodiment

In the embodiment of the present invention, a feature is used in whichtransmission of voice information and verbal information included in thevoice information and nonverbal behavior co-occur when a human performscommunication. Specifically, in the present embodiment, letting at leastone of the voice information of an utterance and the text informationrepresenting the content of the utterance be an input X, and lettingnonverbal information representing the nonverbal behavior of the speakergenerated together with the utterance of the speaker be an output Y, theoutput Y is generated by machine learning from the input X. Thenonverbal information is information related to behavior, and isinformation other than the language itself. Examples of nonverbalbehavior include, for example, the types (classes) of head action, gazedirection, hand gestures, upper body action, lower body action, and thelike.

The nonverbal information obtained in the present embodiment is used ingesture generation and the like in communication robots andcommunication agents that have the same physicality as humans andcommunicate with humans and computer graphics (CG) animation used ingames and interactive systems.

First Embodiment

<Configuration of Nonverbal Information Generation Model LearningApparatus>

FIG. 1 is a block diagram showing an example of the configuration of anonverbal information generation model learning apparatus 10 inaccordance with the first embodiment. As shown in FIG. 1, the nonverbalinformation generation model learning apparatus 10 in accordance withthe present embodiment is configured by a computer provided with acentral processing unit (CPU), a random access memory (RAM), and a readonly memory (ROM) that stores a program for executing a learningprocessing routine described later. The nonverbal information generationmodel learning apparatus 10 is functionally provided with a learninginput unit 20 and a learning calculation unit 30.

The learning input unit 20 receives voice information for learning andnonverbal information for learning that represents information relatingto behavior different from language.

Learning data representing a combination of voice information forlearning and nonverbal information for learning, which are the inputdata of the nonverbal information generation model learning apparatus 10of the present embodiment, is created by acquiring nonverbal information(Y) of a speaker who is speaking using a predetermined measuringapparatus at the same time as acquiring the voice information (X) of thespeaker who is speaking in, for example, the scene shown in FIG. 2. Itshould be noted that the voice information (X) corresponds to voiceinformation when a speaker who is speaking is making externalutterances.

The learning calculation unit 30 generates a nonverbal informationgeneration model for generating time-information-stamped nonverbalinformation, on the basis of the learning data received by the learninginput unit 20. As shown in FIG. 1, the learning calculation unit 30 isprovided with a learning information acquisition unit 31, a learningfeature quantity extraction unit 32, a nonverbal information acquisitionunit 33, a generation parameter extraction unit 34, a learning unit 35,and a learned model storage unit 36.

The learning information acquisition unit 31 acquires the voiceinformation for learning received by the learning input unit 20.Further, the learning information acquisition unit 31 acquires timeinformation indicating the time from a start time to an end time of thevoice information for learning being emitted.

The learning feature quantity extraction unit 32 extractstime-information-stamped voice feature quantities for learning, whichrepresent feature quantities of the voice information for learning, fromthe voice information for learning and time information acquired by thelearning information acquisition unit 31.

For example, the learning feature quantity extraction unit 32 performspredetermined voice information processing on the voice information forlearning acquired by the learning information acquisition unit 31, andextracts the fundamental frequency (F0), power, Mel frequency cepstralcoefficients (MFCC), and the like as voice feature quantities. As shownin FIG. 3, these voice feature quantities can be calculated using awindow width T_(A, w) of an arbitrary time and voice information. Asshown in FIG. 3, these multidimensional voice feature quantities can beexpressed asX _(A) ^(t,T) ^(A,w)   [Expression 1]Here,X _(A) ^(t,T) ^(A,w) [Expression 2]

is a voice feature quantity calculated from the voice informationcorresponding to the window width T_(A,w) from the time t_(A,s). Itshould be noted that the window width does not need to be the same forall voice feature quantities, and the feature quantities may beextracted separately. These methods for extracting voice featurequantities are common, and various techniques have already been proposed(for example, see Reference Document 1). For this reason, any techniquemay be used.

-   [Reference Document 1]: Seiichi Nakagawa, “Spoken Language    Processing and Natural Language Processing”, Mar. 1, 2013, Corona    Publishing Co., Ltd.

The nonverbal information acquiring unit 33 acquires the nonverbalinformation for learning received by the learning input unit 20, andacquires the time information representing the time from the start timeto the end time when behavior represented by the nonverbal informationfor learning is performed.

The nonverbal information acquisition unit 33 acquires informationrelating to nodding, face orientation, hand gestures, gaze, facialexpression, body posture, and the like as nonverbal information forlearning. Examples of parameters representing information relating tonodding, face orientation, hand gestures, gaze, facial expression, bodyposture, and the like are given below.

TABLE 1 Type Parameter Nodding Presence or absence of nodding Y_(N)^(P), number of times Y_(N) ^(T), and depth Y_(N) ^(D) Face orientationAngles of yaw, roll, and pitch (Y_(HD) ^(yaw), Y_(HD) ^(roll), Y_(N)^(pitch) Hand gesture Motion ID (Y_(HG) ^(ID)) Gaze (eyeballs) Positionsof X and Y (Y_(EB) ^(x), Y_(EB) ^(y)) Facial expression (FACS) Strengthof 47 AU (Y_(FACS) ^(i) (i = 1, . . . , 47)) Body posture Front/back,left/right posture position (Y_(BP) ^(FB), Y_(BP) ^(RL))

It should be noted that FACS stands for Facial Action Coding System, andAU stands for Action Unit. For example, in AU1, nonverbal information isrepresented by a label, such as “lifts the inside of eyebrows (AU1)”.Nonverbal information other than the above includes, for example, gazebehavior, head action, breathing action, and mouth shape change of thespeaker.

As described above, the nonverbal information may be any parameterrelated to events such as joints, positions, and movements of the body.Various techniques are conceivable for the measurement technique, andany technique may be used (for example, see Reference Documents 2 and3).

-   [Reference Document 2]: Masaaki Makikawa, Masayuki Nambu, Narihiro    Shiozawa, Shima Okada, and Masaki Yoshida, “Measurement technologies    of mind and body condition in daily life for the development of    human friendly products”, Oct. 1, 2010, Corona Publishing Co., Ltd.-   [Reference Document 3]: Shihong Xia, Lin Gao, Yu-Kun Lai, Ming-Ze    Yuan, and Jinxiang Chai, “A Survey on Human Performance Capture and    Animation”, Journal of Computer Science and Technology, Volume 32,    Issue 3, pp. 536-554, (2017).

As shown in FIG. 2, with regard to for example the orientation of aface, the head direction of a human or a conversational humanoid can bemeasured by a behavior capture apparatus such as a head measurementapparatus (head tracker). It is also possible to acquire the orientationof a face from animation data such as CG. Further, as shown in FIG. 2,the orientation of a face is represented by, for example, the angles ofthe three axes of Yaw, Roll, and Pitch.

The generation parameter extraction unit 34 discretizes a parameter(sensory scale) represented by nonverbal information for learningacquired by the nonverbal information acquisition unit 33, and extractsthe time-information-stamped discretized nonverbal information. Forexample, facial orientation is represented by Yaw, Roll, and Pitch angleinformation, and thus arbitrary thresholds α and β (α<β) may bedetermined in advance and it is converted into a nominal scale as shownbelow. It should be noted that only Yaw is presented in the followingexample.−α<Yaw<α: frontα≤Yaw<β: facing slightly leftβ<Yaw≤−α: facing greatly left−β<Yaw≤−α: facing slightly right−β≥Yaw: facing greatly right

In this way, the nonverbal information for learning acquired by thenonverbal information acquisition unit 33 is discretized, and that towhich time information is assigned is converted into a multidimensionalvectorY ^(t) ^(N,s) ^(,t) ^(N,e)   [Expression 3]

Here, t_(N,s), t_(N,e) are the start time and the end time at which thenonverbal information is obtained, respectively.

The learning unit 35 learns a nonverbal information generation model forgenerating time-information-stamped nonverbal information fromtime-information-stamped voice feature quantities on the basis of thetime-information-stamped voice feature quantities for learning extractedby the learning feature quantity extraction unit 32, andtime-information-stamped discretized nonverbal information acquired bythe generation parameter extraction unit 34.

Specifically, the learning unit 35 constructs a nonverbal informationgeneration model that takes time-information-stamped voice featurequantities for learning extracted by the learning feature quantityextraction unit 32X _(A) ^(t,T) ^(A,w) [Expression 4]

as input and outputs time-information-stamped nonverbal informationY ^(t) ^(N,s) ^(,t) ^(N,e)   [Expression 5]

In constructing the nonverbal information generation model, any machinelearning technique may be used, but a support vector machine (SVM) isused in the present embodiment. For example, using an SVM, a classifierfor the parameters of each dimension inY ^(t) ^(N,s) ^(,t) ^(N,e) [Expression 6]is constructed, or a regression model by support vector machine forregression (SVR) in which an SVM is applied to regression isconstructed.

In addition, in the present embodiment, for each type of actionrepresented by the nonverbal information, an SVM model for estimatingthe presence or absence of the type of action is created.

It should be noted that in the nonverbal information generation model,whether to estimate the nonverbal information at what time resolutionand using what time parameters is arbitrary. Here is shown an example ofa feature quantity used in the case of estimating a gestureY ^(T1,T2)  [Expression 7]at an arbitrary time section T1 to T2. The voice feature quantitiesX _(A) ^(T1,T) ^(A,w) ˜X _(A) ^(T2,T) ^(A,w)   [Expression 8]obtained at times T1 to T2, which are the target of estimation, and thegesture to be outputY ^(T1,T2)  [Expression 9]are paired, and learning is performed using learning data including aplurality of sets of data of these pairs. Then letM ^(T1,T2)  [Expression 10]be the learned nonverbal information generation model.

The learned model storage unit 36 stores the learned nonverbalinformation generation model learned by the learning unit 35. Thelearned nonverbal information generation model generatestime-information-stamped nonverbal information from thetime-information-stamped voice feature quantities.

<System Configuration of Nonverbal Information Generation Apparatus>

FIG. 4 is a block diagram showing an example of the configuration of anonverbal information generation apparatus 40 in accordance with thefirst embodiment. As shown in FIG. 4, the nonverbal informationgeneration apparatus 40 in accordance with the present embodiment isconfigured by a computer provided with a central processing unit (CPU),a random access memory (RAM), and a read only memory (ROM) that stores aprogram for executing a nonverbal information generation processingroutine described later. The nonverbal information generation apparatus40 is functionally provided with an input unit 50, a calculation unit60, and an expression unit 70.

The input unit 50 receives voice information and time informationindicating the time from a start time to an end time of the voiceinformation being emitted.

The calculation unit 60 is provided with an information acquisition unit61, a feature quantity extraction unit 62, a learned model storage unit63, and a nonverbal information generation unit 64.

The information acquisition unit 61 acquires the voice information andthe time information indicating the time from the start time to the endtime when the voice information is emitted, which are received by theinput unit 50.

Similarly to the learning feature quantity extraction unit 32, thefeature quantity extraction unit 62 extracts time-information-stampedvoice feature quantities, indicating feature quantities of the voiceinformation, from the voice information and the time informationacquired by the information acquisition unit 61.

The learned model storage unit 63 stores the same learned nonverbalinformation generation model as the learned nonverbal informationgeneration model stored in the learned model storage unit 36.

The nonverbal information generation unit 64 generatestime-information-stamped nonverbal information corresponding to thetime-information-stamped voice feature quantities extracted by thefeature quantity extraction unit 62 on the basis of thetime-information-stamped voice feature quantities extracted by thefeature quantity extraction unit 62 and the learned nonverbalinformation generation model stored in the learned model storage unit63.

For example, the nonverbal information generation unit 64, using thelearned nonverbal information generation modelM ^(T1,T2)  [Expression 11]stored in the learned model storage unit 63, receives an input ofarbitrary feature quantities as time-information-stamped voice featurequantitiesX _(A) ^(T1,T) ^(A,w) ˜X _(A) ^(T2,T) ^(A,w)   [Expression 12]to acquire a gestureY ^(T1,T2)  [Expression 13]as time-information-stamped nonverbal information.

Then, the nonverbal information generating unit 64 controls theexpression unit 70 so that the time-information-stamped nonverbalinformation that has been generated is output from the expression unit70 on the basis of the time information assigned to the nonverbalinformation.

Specifically, the nonverbal information generating unit 64 causes thegestureY ^(T1,T2)  [Expression 14]to be reflected as an action of an arbitrary target (for example, ananimation character, a robot, or the like) in the expression unit 70.

The expression unit 70 causes the voice information received by theinput unit 50 and the nonverbal information generated by the nonverbalinformation generation unit 64 to be expressed under the control of thenonverbal information generation unit 64.

Examples of the expression unit 70 include a communication robot, acommunication agent displayed on a display, a CG animation used in agame and an interactive system, and the like.

<Operation of Nonverbal Information Generation Model Learning Apparatus10>

Next, the operation of the nonverbal information generation modellearning apparatus 10 in accordance with the present embodiment will bedescribed. First, when learning data representing a combination of aplurality of pieces of voice information for learning and a plurality ofpieces of nonverbal information for learning are input to the learninginput unit 20 of the nonverbal information generation model learningapparatus 10, the nonverbal information generation model learningapparatus 10 executes the learning processing routine shown in FIG. 5.

First, in Step S100, the learning information acquiring unit 31acquires, from among the plurality of sets of learning data received bythe learning input unit 20, the voice information for learning and thetime information indicating the time from the start time to the end timeof the voice information for learning being emitted.

In Step S102, the nonverbal information acquisition unit 33 acquires,from among the plurality of sets of learning data received by thelearning input unit 20, the nonverbal information for learning and thetime information indicating the time from the start time to the end timewhen the behavior represented by the nonverbal information for learningis performed.

In Step S104, the learning feature quantity extraction unit 32 extractstime-information-stamped voice feature quantities for learning, from thevoice information for learning and the time information acquired in StepS100.

In Step S106, the generation parameter extraction unit 34 extractstime-information-stamped discretized nonverbal information from thenonverbal information for learning and the time information acquired inStep S102.

In Step S108, the learning unit 35 learns a nonverbal informationgeneration model for generating time-information-stamped nonverbalinformation from the time-information-stamped voice feature quantitieson the basis of the time-information-stamped voice feature quantitiesfor learning extracted in Step S104 and the time-information-stampednonverbal information acquired in Step S106.

In Step S110, the learning unit 35 stores the learned nonverbalinformation generation model obtained in Step S108 in the learned modelstorage unit 36, and ends the learning processing routine.

<Operation of Nonverbal Information Generation Apparatus 40>

Next, the operation of the nonverbal information generation apparatus 40in accordance with the present embodiment will be described. First, whenthe learned nonverbal information generation model stored in the learnedmodel storage unit 36 of the nonverbal information generation modellearning apparatus 10 is input to the nonverbal information generationapparatus 40, the learned nonverbal information generation model isstored in the learned model storage unit 63 of the nonverbal informationgeneration apparatus 40. Then, when voice information that is a targetof nonverbal information generation is input to the input unit 50, thenonverbal information generation apparatus 40 executes the nonverbalinformation generation processing routine shown in FIG. 6.

In Step S200, the information acquisition unit 61 acquires the voiceinformation and the time information representing the time from thestart time to the end time when the voice information is emitted, whichhave been received by the input unit 50.

In Step S202, the feature quantity extraction unit 62 extracts thetime-information-stamped voice feature quantities from the voiceinformation and time information acquired in Step S200, similarly to thelearning feature quantity extraction unit 32.

In Step S204, the nonverbal information generation unit 64 reads thelearned nonverbal information generation model stored in the learnedmodel storage unit 63.

In Step S206, the nonverbal information generation unit 64 generatestime-information-stamped nonverbal information corresponding to thetime-information-stamped voice feature quantities extracted in StepS202, on the basis of the time-information-stamped voice featurequantities extracted in Step S202 and the learned nonverbal informationgeneration model read in Step S204.

In Step S208, the nonverbal information generation unit 64 controls theexpression unit 70 such that the time-information-stamped nonverbalinformation generated in Step S206 is output from the expression unit 70on the basis of the time information assigned to the nonverbalinformation, and ends the nonverbal information generation processingroutine.

As described above, the nonverbal information generation apparatus 40 inaccordance with the first embodiment extracts time-information-stampedvoice feature quantities from the voice information and the timeinformation, and generates time-information-stamped nonverbalinformation corresponding to the time-information-stamped voice featurequantities on the basis of the time-information-stamped voice featurequantities that have been extracted and the learned nonverbalinformation generation model for generating time-information-stampednonverbal information. Thereby, the voice information and the nonverbalinformation are automatically associated, and so a cost reduction can beachieved.

When a communication robot, a conversational agent, or the like is madeto perform actions such as gestures in accordance with uttered voice orof text corresponding thereto, it is necessary to decide what kind ofaction should be performed and at what sort of timing in accordance withthe utterance. Conventionally, these have all been manually created andset as scenarios, leading to a high production cost.

In contrast, in the present embodiment, by generating a learnednonverbal information generation model for generatingtime-information-stamped nonverbal information fromtime-information-stamped voice feature quantities, with voiceinformation as input, time-information-stamped nonverbal information(nonverbal information to which output timing has been assigned)corresponding to an action that corresponds to the input is output.

Thereby, with the present embodiment, it becomes possible toautomatically generate nonverbal information from voice information, andthus it is not necessary to individually register nonverbal informationfor an utterance as in the conventional art, and so costs are greatlyreduced. Further, by using the present embodiment, it is possible togenerate nonverbal behavior at a human-like natural timing for the inputvoice information. Thereby, advantageous effects are attained such as animprovement in the human-like nature and naturalness of agents, robots,and the like, facilitation of transmission of intention by nonverbalbehavior, enlivening of conversation, and the like.

In addition, by using a nonverbal information generation model learnedin advance, an uttered voice or text serves as input, with informationon an action corresponding to the input and the timing thereof beingoutput. Thereby, scenario creation costs can be reduced. Also, since theaction is generated based on actual human actions, the action can bereproduced with a more natural timing.

Moreover, the nonverbal information generation model learning apparatus10 in accordance with the first embodiment learns a nonverbalinformation generation model for generating time-information-stampednonverbal information from the time-information-stamped voice featurequantities on the basis of the time-information-stamped voice featurequantities for learning and the time-information-stamped nonverbalinformation for learning. Thereby, it is possible to obtain a nonverbalinformation generation model for generating nonverbal information fromvoice feature quantities, while reducing the cost of associating voiceinformation with nonverbal information.

Also, by using a learned nonverbal information generation model, it ispossible to generate nonverbal behavior at a natural timing.

In the first embodiment, the case in which nonverbal information isgenerated from voice feature quantities has been described as anexample. In the first embodiment, it is possible to generate nonverbalinformation with a minimum necessary configuration based on informationexpressed as voice feature quantities (for example, an emotion or thelike) without delving into the spoken content.

It should be noted that in the first embodiment, since the content beingspoken is not delved into (verbal information is not used), for example,a sensor may be attached to an animal to acquire nonverbal informationand voice information (for example, cries and the like), and then ananimal-type robot may be operated.

Second Embodiment

<Configuration of Nonverbal Information Generation Model LearningApparatus>

Next, a second embodiment of the present invention will be described. Itshould be noted that components with the same configuration as those inthe first embodiment are denoted by the same reference signs, withdescriptions thereof being omitted.

In the second embodiment, text information is used as input instead ofvoice information. The difference from the first embodiment is thatlearning of a nonverbal information generation model for generatingnonverbal information from text information is performed. It should benoted that the text information used in the second embodiment is textinformation indicating uttered content, when a speaker is speakingexternally via voice.

FIG. 7 is a block diagram showing an example of the configuration of anonverbal information generation model learning apparatus 210 inaccordance with the second embodiment. As shown in FIG. 7, the nonverbalinformation generation model learning apparatus 210 in accordance withthe second embodiment is configured by a computer provided with a CPU, aRAM, and a ROM storing a program for executing a learning processingroutine described later. The nonverbal information generation modellearning apparatus 210 is functionally provided with a learning inputunit 220 and a learning calculation unit 230.

The learning input unit 220 receives text information for learning andnonverbal information for learning.

The learning calculation unit 230 generates a nonverbal informationgeneration model for generating time-information-stamped nonverbalinformation on the basis of the learning data received by the learninginput unit 220. As illustrated in FIG. 7, the learning calculation unit230 is provided with a learning information acquisition unit 231, alearning feature quantity extraction unit 232, a nonverbal informationacquisition unit 33, a generation parameter extraction unit 34, alearning unit 235, and a learned model storage unit 236.

The learning information acquisition unit 231 acquires voice informationfor learning corresponding to the text information for learning, andacquires time information indicating the time from a start time to anend time of the voice information being emitted. As shown in FIG. 8, thelearning information acquisition unit 231 is provided with a learningtext analysis unit 237 and a learning voice synthesis unit 238.

The learning text analysis unit 237 performs a predetermined textanalysis on the text information for learning, and acquires a result ofthe text analysis. For example, the learning text analysis unit 237performs text analysis such as morphological analysis on the textinformation for learning, and for each morpheme extracts word notation(morpheme) information, a part of speech, category information, anevaluative expression, an emotional expression, a sensibilityexpression, sound onomatopoeia/mimetic word/voice onomatopoeia, a namedentity, a theme, the number of characters, position, thesaurusinformation, and the like, and for each sentence extracts the dialogueact of the utterance. It should be noted that word notation (morpheme)information, a part of speech, category information, an evaluativeexpression, an emotional expression, a named entity, a theme, the numberof characters, position, thesaurus information, and the like may beextracted for each clause instead of each morpheme. Also, word notation(morpheme) information, a part of speech, category information, anevaluative expression, an emotional expression, a sensibilityexpression, sound onomatopoeia/mimetic word/voice onomatopoeia, a namedentity, a theme, number of characters, position, and thesaurusinformation and the like may be extracted in arbitrary units other thanmorphemes and clauses. For example, the extraction may be in units ofcharacters, and in the case of English, may be in units of characterstrings delimited by spaces, or in units of phrases. Also, theextraction of a theme may be performed for each sentence or eachutterance. Here, a dialogue act is an abstraction of intention in anutterance and an abstraction serving as a label. A theme is informationindicating a topic or a focus in the text. The number of characters isthe number of characters in a morpheme or a clause. The position refersto a position of a morpheme or a clause from the beginning or end of asentence. Thesaurus information refers to thesaurus information of amorpheme or a word in a clause based on the Japanese Lexicon. The methodfor extracting these text feature quantities may be a general one, andvarious techniques have already been proposed (see Reference Document 1above and Reference Documents 4 to 6 below). In the present embodiment,an example will be described for the case of using, among these types ofinformation, word notation (morpheme) information, a part of speech, adialogue act, the number of characters, position, and thesaurusinformation.

-   [Reference Document 4]: R. Higashinaka, K. Imamura, T. Meguro, C.    Miyazaki, N. Kobayashi, H. Sugiyama, T. Hirano, T. Makino, and Y.    Matsuo, “Towards an open-domain conversational system fully based on    natural language processing”, In Proceedings of International    conference on Computational linguistics, pp. 928-939, 2014-   [Reference Document 5]: Japanese Unexamined Patent Application First    Publication No. 2014-222399-   [Reference Document 6] Japanese Unexamined Patent Application First    Publication No. 2015-045915

The learning voice synthesis unit 238 synthesizes voice information forlearning corresponding to the text information for learning, on thebasis of the text analysis result acquired by the learning text analysisunit 237. For example, the learning voice synthesis unit 238 performsvoice synthesis using the text analysis result, generates an utterancecorresponding to the text information, and sets the utterance as voiceinformation for learning corresponding to the text information forlearning.

Also, the learning voice synthesis unit 238 acquires time informationrepresenting the time from the start time to the end time of the voiceinformation for learning being emitted. Specifically, the learning voicesynthesis unit 238 acquires time information corresponding to the starttime to the end time of the voice of the utterance generated by thevoice synthesis. This time information corresponds to each morpheme ofthe text information corresponding to the utterance. It should be notedthat the start time and the end time may also be obtained for eachcharacter included in the text information.

The learning feature quantity extraction unit 232 extractstime-information-stamped text feature quantities for learning, whichrepresent feature quantities of the text information for learning, fromthe text information for learning and the time information acquired bythe learning voice synthesis unit 238. Specifically, the learningfeature quantity extraction unit 232 assigns time information to thetext information for each predetermined analysis unit, and extractstime-information-stamped text feature quantities.

Specifically, the learning feature quantity extraction unit 232 performssentence partition on the text information for learning output by thelearning voice synthesis unit 238. Next, the learning feature quantityextraction unit 232 extracts text feature quantitiesX _(D) ^(t) ^(S,s) ^(,t) ^(S,e)   [Expression 15]related to the dialogue act obtained by the learning text analysis unit237 for each sentence. It should be noted thatt _(S,s) ,t _(S,e)  [Expression 16]is the start time and end time of utterance corresponding to onesentence.

Moreover, for each of a plurality of morphemes constituting eachsentence obtained by the partitioning, the learning feature quantityextraction unit 232 extracts at least the word notation information,among the word notation information, the part of speech, the categoryinformation (for example, noun, named entity, or declinable word), theevaluative expression, the emotional expression, the named entity, thenumber of characters, the position in the sentence, the thesaurusinformation, and the like. Then, the learning feature quantityextraction unit 232 puts these multidimensional feature quantities intothe form ofX _(P) ^(t) ^(P,s) ^(,t) ^(P,e)   [Expression 17]

It should be noted thatt _(P,s) ,t _(P,e)  [Expression 18]are the start time and end time of the uttered voice corresponding tothe morpheme unit, respectively. FIG. 3 shows an example oftime-information-stamped text feature quantities. The start time and endtime of each morpheme of the text information are obtained as shown inFIG. 3.

It should be noted that for each of the plurality of clausesconstituting each sentence obtained by the partitioning, the wordnotation information, part of speech, category information (for example,noun, named entity, declinable word, and the like), evaluativeexpression, emotional expression, named entity, number of characters,position in the sentence, thesaurus information, and the like may beextracted. Then, the learning feature quantity extraction unit 232 putsthese multidimensional feature quantities into the form ofX _(C) ^(t) ^(C,s) ^(,t) ^(C,e)   [Expression 19]

It should be noted thatt _(C,s) ,t _(C,e)  [Expression 20]are the start time and end time of uttered voice corresponding to aclause unit, respectively.

In the learning information acquisition unit 231, information obtainedwhen performing voice recognition and voice synthesis may be diverted.

The learning unit 235 learns a nonverbal information generation modelfor generating time-information-stamped nonverbal information from thetime-information-stamped text feature quantities, on the basis of thetime-information-stamped text feature quantities for learning extractedby the learning feature quantity extraction unit 232, and thetime-information-stamped discretized nonverbal information for learningextracted by the generation parameter extraction unit 34.

Specifically, the learning unit 235 constructs a nonverbal informationgeneration model that takes the time-information-stamped text featurequantities for learning extracted by the learning feature extractingunit 232X _(D) ^(t) ^(S,s) ^(,t) ^(S,e)   [Expression 21]X _(P) ^(t) ^(P,s) ^(,t) ^(P,e)   [Expression 22]as inputs, and outputs nonverbal informationY ^(t) ^(N,s) ^(,T) ^(N,e) [Expression 23]

When constructing the nonverbal information generation model, anymachine learning technique may be used, and SVM is used in the presentembodiment.

It should be noted that in the nonverbal information generation model,what kind of time resolution is used and which time parameter is used toestimate the nonverbal information are arbitrary. Here is shown anexample of a feature quantity used in the case of estimating a gestureY ^(T1,T2)  [Expression 24]

in an arbitrary time section T1 to T2. The verbal feature quantitiesX _(D) ^(T1,T2)

X _(P) ^(T1,T2)  [Expression 25]and the gesture to be outputY ^(T1,T2)  [Expression 26]obtained in the time between times T1 to T2, which is the target ofestimation, are paired, and learning is performed using learning dataincluding a plurality of sets of data of these pairs. The learnednonverbal information generation model becomesM ^(T1,T2)  [Expression 27]

It should be noted that as a setting method of T1 and T2, for example,when nonverbal information is estimated in morpheme units, the starttime and end time of each morpheme are set to T and T2, respectively. Inthis case, the window width from T2 to T1 differs for each morpheme.

The learned model storage unit 236 stores the learned nonverbalinformation generation model learned by the learning unit 235. Thelearned nonverbal information generation model generatestime-information-stamped nonverbal information from thetime-information-stamped text feature quantities.

<Configuration of Nonverbal Information Generation Apparatus>

FIG. 9 is a block diagram illustrating an example of the configurationof a nonverbal information generation apparatus 240 in accordance withthe second embodiment. As shown in FIG. 9, the nonverbal informationgeneration apparatus 240 in accordance with the present embodiment isconfigured by a computer provided with a central processing unit (CPU),a random access memory (RAM), and a read only memory (ROM) that stores aprogram for executing a nonverbal information generation processingroutine described later. The nonverbal information generation apparatus240 is functionally provided with an input unit 250, a calculation unit260, and an expression unit 70.

The input unit 250 receives text information.

The calculation unit 260 is provided with an information acquisitionunit 261, a feature quantity extraction unit 262, a learned modelstorage unit 263, and a nonverbal information generation unit 264.

The information acquisition unit 261 acquires the text informationreceived by the input unit 250. Further, the information acquisitionunit 261 acquires voice information corresponding to the textinformation, and acquires time information representing the time from astart time to an end time of the voice information being emitted. Asshown in FIG. 10, the information acquisition unit 261 is provided witha text analysis unit 265 and a voice synthesis unit 266.

Similarly to the learning text analysis unit 237, the text analysis unit265 performs a predetermined text analysis on the text informationreceived by the input unit 250, and acquires a result of the textanalysis.

Similarly to the learning voice synthesis unit 238, the voice synthesisunit 266 synthesizes voice information corresponding to the textinformation on the basis of the text analysis result obtained by thetext analysis unit 265. Then, the voice synthesis unit 266 acquires timeinformation corresponding to the start time to the end time of the voiceof the utterance generated by the voice synthesis.

Similarly to the learning feature quantity extraction unit 232, thefeature quantity extraction unit 262 extracts time-information-stampedtext feature quantities representing feature quantities of the textinformation from the text information and the time information acquiredby the information acquisition unit 261.

The same learned nonverbal information generation model as the learnednonverbal information generation model stored in the learned modelstorage unit 236 is stored in the learned model storage unit 263.

The nonverbal information generation unit 264 generatestime-information-stamped nonverbal information corresponding to thetime-information-stamped text feature quantities extracted by thefeature quantity extraction unit 262, on the basis of thetime-information-stamped text feature quantities extracted by thefeature quantity extraction unit 262 and the learned nonverbalinformation generation model stored in the learned model storage unit263.

For example, the nonverbal information generation unit 264, using thelearned nonverbal information generation modelM ^(T1,T2)  [Expression 28]stored in the learned model storage unit 263, receives an input ofarbitrary feature quantities as a time-information-stamped text featurequantitiesX _(D) ^(T1,T2)

X _(P) ^(T1,T2)  [Expression 29]to acquire a gestureY ^(T1,T2)  [Expression 30]as a generation parameter corresponding to time-information-stampednonverbal information.

Then, the nonverbal information generation unit 264 controls theexpression unit 70 so that the time-information-stamped generationparameter that has been generated is output from the expression unit 70.

Specifically, the nonverbal information generation unit 264 causes thegestureY ^(T1,T2)  [Expression 31]

to be reflected as an action of an arbitrary target (for example, ananimation character, a robot, or the like) in the expression unit 70.

The expression unit 70 causes the voice information corresponding to thetext information received by the input unit 250 and the nonverbalinformation generated by the nonverbal information generation unit 264to be expressed under the control of the nonverbal informationgeneration unit 264.

<Operation of Nonverbal Information Generation Model Learning Apparatus210>

Next, the operation of the nonverbal information generation modellearning apparatus 210 in accordance with the second embodiment will bedescribed. First, when learning data representing a combination of aplurality of pieces of text information for learning and a plurality ofpieces of nonverbal information for learning is input to the learninginput unit 220 of the nonverbal information generation model learningapparatus 210, the nonverbal information generation model learningapparatus 210 executes the learning processing routine shown in FIG. 11.

First, in Step S300, the learning information acquisition unit 231acquires text information for learning from among the plurality of setsof learning data received by the learning input unit 220 (specifically,pairs of text information and nonverbal information).

In Step S303, the learning text analysis unit 237 performs apredetermined text analysis on the text information for learningacquired in Step S300 and acquires a result of the text analysis.Further, the learning voice synthesis unit 238 synthesizes voiceinformation for learning corresponding to the text information forlearning on the basis of the text analysis result acquired by thelearning text analysis unit 237. Then, the learning voice synthesis unit238 acquires time information representing the time from the start timeto the end time when the voice information for learning is emitted.

In Step S304, the learning feature quantity extraction unit 232 extractstime-information-stamped text feature quantities for learning from thetext information for learning and time information acquired in StepS303.

In Step S308, the learning unit 235 learns a nonverbal informationgeneration model for generating a time-information-stamped generationparameter from the time-information-stamped text feature quantities, onthe basis of the time-information-stamped text feature quantities forlearning extracted in Step S304 and the time-information-stampedgeneration parameter for learning acquired in Step S106.

<Operation of Nonverbal Information Generation Apparatus 240>

Next, the operation of the nonverbal information generation apparatus240 in accordance with the second embodiment will be described. First,when the learned nonverbal information generation model stored in thelearned model storage unit 236 of the nonverbal information generationmodel learning apparatus 210 is input to the nonverbal informationgeneration apparatus 240, the learned nonverbal information generationmodel is stored in the learned model storage unit 263 of the nonverbalinformation generation apparatus 240. Then, when text information thatis the target of nonverbal information generation is input to the inputunit 250, the nonverbal information generation apparatus 240 executesthe nonverbal information generation processing routine shown in FIG.12.

In Step S400, the information acquisition unit 261 acquires the textinformation received by the input unit 250.

In Step S401, the text analysis unit 265 performs a predetermined textanalysis on the text information acquired in Step S400, and acquires aresult of the text analysis. Further, the voice synthesis unit 266synthesizes voice information corresponding to the text information onthe basis of the text analysis result obtained by the text analysis unit265. Then, the voice synthesizing unit 266 acquires time informationrepresenting the time from the start time to the end time when the voiceinformation is emitted.

In Step S402, the feature quantity extraction unit 262 extractstime-information-stamped text feature quantities from the textinformation and the time information acquired in Step S401.

In Step S404, the nonverbal information generation unit 264 reads thelearned nonverbal information generation model stored in the learnedmodel storage unit 263.

In Step S406, the nonverbal information generation unit 264 generates atime-information-stamped generation parameter corresponding to thetime-information-stamped text feature quantities extracted in Step S402,on the basis of the time-information-stamped text feature quantitiesextracted in Step S402 and the learned nonverbal information generationmodel read in Step S404.

It should be noted that since other configurations and operations of thenonverbal information generation apparatus and the nonverbal informationgeneration model learning apparatus in accordance with the secondembodiment are the same as those in the first embodiment, descriptionsthereof will be omitted.

As described above, the nonverbal information generation apparatus 240in accordance with the second embodiment acquires voice informationcorresponding to text information, acquires time informationrepresenting the time from the start time to the end time when the voiceinformation is emitted, and generates time-information-stamped nonverbalinformation corresponding to the time-information-stamped text featurequantities on the basis of the time-information-stamped text featurequantities and the learned model for generating time-information-stampednonverbal information. This makes it possible to automatically associatethe text information with the nonverbal information and to reduce thecost of doing so.

Also, in the present embodiment, by generating a learned nonverbalinformation generation model for generating time-information-stampednonverbal information from the time-information-stamped text featurequantities, text information is set as input, andtime-information-stamped nonverbal information (nonverbal information towhich the output timing has been assigned) corresponding to an actionthat corresponds to the input is output.

Thereby, with the present embodiment, it is possible to automaticallygenerate nonverbal information from text information, and thus it is notnecessary to individually register nonverbal information for anutterance as in the conventional art, and so costs are greatly reduced.Further, by using the present embodiment, it is possible to generatenonverbal behavior at a human-like natural timing for the input textinformation. Thereby, advantageous effects are attained such as animprovement in the human-like nature and naturalness of agents, robots,or the like, facilitation of transmission of intention by nonverbalbehavior, enlivening of conversation, and the like.

Further, the nonverbal information generation model learning apparatus210 in accordance with the second embodiment learns a nonverbalinformation generation model for generating time-information-stampednonverbal information from the time-information-stamped text featurequantities on the basis of the time-information-stamped text featurequantities for learning and the time-information-stamped nonverbalinformation for learning. Thereby, it is possible to obtain a nonverbalinformation generation model for generating nonverbal information fromtext feature quantities while reducing the cost of associating textinformation with nonverbal information.

Further, in the second embodiment described above, the case in whichnonverbal information is generated from text feature quantities has beendescribed as an example. In the second embodiment described above, it ispossible to generate nonverbal information by using information such asword notation, parts of speech, and dialogue acts as clues. By usingsuch a configuration, it is possible to generate nonverbal informationwith the minimum required configuration when the input does not involvevoice, such as dialogue in a chat.

Third Embodiment

Next, a third embodiment of the present invention will be described. Itshould be noted that components with the same configuration as those inthe first or second embodiment are denoted by the same reference signs,with descriptions thereof being omitted.

In the third embodiment, both voice information and text information areused as inputs. The difference from the first and second embodiments isthat nonverbal information is generated from voice information and textinformation. It should be noted that the text information used in thethird embodiment is text information representing uttered content, whena speaker is speaking externally via voice.

FIG. 13A shows a configuration example of a learning informationacquisition unit 331 and a learning feature quantity extraction unit 332in the nonverbal information generation model learning apparatus of thethird embodiment.

As shown in FIG. 13A, when voice information for learning is input, alearning voice recognition unit 337 performs a predetermined voicerecognition process on the voice information for learning and acquirestext information corresponding to the voice information for learning(hereinafter referred to as recognition text for learning).

Then, a learning text analysis unit 338 performs a predetermined textanalysis on the recognition text for learning and acquires a result ofthe text analysis.

Then, the learning feature quantity extraction unit 332 extractstime-information-stamped voice feature quantities from the voiceinformation for learning. The learning feature quantity extraction unit332 additionally extracts time-information-stamped text featurequantities from the recognition text for learning.

Then, the learning unit (not shown) of the third embodiment learns anonverbal information generation model on the basis of thetime-information-stamped voice feature quantities for learning and thetime-information-stamped text feature quantities for learning, and thenonverbal information for learning. Thereby, it is possible to obtain anonverbal information generation model for generatingtime-information-stamped nonverbal information from thetime-information-stamped voice feature quantities and thetime-information-stamped text feature quantities.

FIG. 13B shows a configuration example of an information acquisitionunit 361 and a feature quantity extraction unit 362 in the nonverbalinformation generation apparatus of the third embodiment.

As shown in FIG. 13B, when voice information is input, a voicerecognition unit 365 performs a predetermined voice recognition processon the voice information, and acquires text information corresponding tothe voice information (hereinafter, referred to as recognition text).Then, a text analysis unit 366 performs a predetermined text analysis onthe recognition text and acquires a result of the text analysis.

Then, the feature quantity extraction unit 362 extractstime-information-stamped voice feature quantities from the voiceinformation. The feature quantity extraction unit 362 also extractstime-information-stamped text feature quantities from the recognitiontext.

Then, a nonverbal information generation unit (not shown) of the thirdembodiment generates time-information-stamped nonverbal information onthe basis of the time-information-stamped voice feature quantities, thetime-information-stamped text feature quantities, and the learnednonverbal information generation model. Thereby, it is possible toappropriately generate the nonverbal information using both thetime-information-stamped voice feature quantities and thetime-information-stamped text feature quantities.

It should be noted that when generating the nonverbal information usingboth the voice feature quantities and the text feature quantities, it ispreferable that there be correspondence and agreement between the voicefeature quantities and the text feature quantities on the time axis ofthe time information, as shown in FIG. 3.

In addition, when generating the nonverbal information using both thevoice feature quantities and the text feature quantities and expressingthe nonverbal information with the expression unit, it is also possibleto present the voice information or text information serving as inputtogether with synthesized voice obtained from the text information, orrecognition text obtained from the voice information.

As described above, the nonverbal information generation apparatus inaccordance with the third embodiment generates time-information-stampednonverbal information on the basis of the time-information-stamped voicefeature quantities and the time-information-stamped text featurequantities, and the learned nonverbal information generation model forgenerating time-information-stamped nonverbal information. Thereby, itis possible to reduce the cost of associating voice information and textinformation with nonverbal information.

Further, the nonverbal information generation model learning apparatusin accordance with the third embodiment can obtain a nonverbalinformation generation model for generating time-information-stampednonverbal information from the time-information-stamped voice featurequantities and the time-information-stamped text feature quantities onthe basis of time-information-stamped voice feature quantities forlearning and the time-information-stamped text feature quantities forlearning.

It should be noted that in the third embodiment described above, thecase in which the input information is voice information has beendescribed as an example, but the present invention is not limitedthereto. For example, the input information may be text information.

FIG. 14A shows a configuration example of a learning informationacquisition unit 431 and a learning feature quantity extraction unit 432in the nonverbal information generation model learning apparatus whenthe input information is text information.

As illustrated in FIG. 14A, when text information for learning is input,a learning text analysis unit 437 performs a predetermined text analysisprocess on the text information for learning and obtains a result of thetext analysis corresponding to the text information for learning.

Then, a learning voice synthesis unit 438 performs a predetermined voicesynthesis process on the text analysis result and acquires voiceinformation for learning.

Then, the learning feature quantity extraction unit 432 extractstime-information-stamped voice feature quantities from the voiceinformation for learning. Further, the learning feature quantityextraction unit 432 extracts time-information-stamped text featurequantities from the text information for learning.

The learning unit (not shown) learns a nonverbal information generationmodel on the basis of the time-information-stamped voice featurequantities for learning, the time-information-stamped text featurequantities for learning, and the nonverbal information for learning.Thereby, it is possible to obtain a nonverbal information generationmodel for generating time-information-stamped nonverbal information fromthe time-information-stamped voice feature quantities and thetime-information-stamped text feature quantities.

Further, FIG. 14B shows a configuration example of an informationacquisition unit 461 and a feature quantity extraction unit 462 in thenonverbal information generation apparatus when the input information istext information.

As shown in FIG. 14B, when text information is input, a text analysisunit 465 performs a predetermined text analysis process on the textinformation and acquires a result of the text analysis corresponding tothe text information.

Then, a voice synthesis unit 466 performs a predetermined voicesynthesis process on the text analysis result and acquires voiceinformation.

Then, the feature quantity extraction unit 462 extractstime-information-stamped voice feature quantities from the voiceinformation. Further, the feature quantity extraction unit 462 extractstime-information-stamped text feature quantities from the textinformation.

Then, the nonverbal information generation unit (not shown) acquires ageneration parameter representing nonverbal information on the basis ofthe time-information-stamped voice feature quantities, thetime-information-stamped text feature quantities, and the nonverbalinformation generation model learned in advance.

It should be noted that the present invention is not limited to theabove-described embodiments, and various modifications and applicationsare possible without departing from the gist of the present invention.

For example, as shown in FIG. 15, there are a total of four patterns ofthe configuration corresponding to a combination of the informationacquisition unit (or the learning information acquisition unit) and thefeature quantity extraction unit (or the learning feature quantityextraction unit) in each of the above-described embodiments. Inaddition, the patterns shown in FIG. 16 are possible variations ascombinations of configurations during learning and during nonverbalinformation generation. It should be noted that the accuracy at the timeof nonverbal information generation is higher when the featurequantities at the time of learning and at the time of generation are thesame.

The present invention can also be realized by installing a program in awell-known computer via a medium or a communication line.

Further, although the above-described apparatuses have a computer systeminside, if the “computer system” uses a World Wide Web (WWW) system, thecomputer system may include a homepage providing environment (or displayenvironment).

Further, in the specification of the present application, an embodimentin which the program is preinstalled has been described, but the programcan also be provided by being stored in a computer-readable recordingmedium.

It should be noted that other embodiments will be described below.

Outline of Other Embodiments

Learning data used in the nonverbal information generation modellearning apparatus is created by using a measuring apparatus to acquirenonverbal information (Y) of a conversation partner who is aninterlocutor of a speaker who is speaking, at the same time as acquiringvoice information (X) of the speaker who is speaking, in for example thescene depicted in FIG. 2.

When performing learning of a nonverbal information generation model onthe basis of learning data created in this way, it becomes possible torealize agents, robots, and the like that perform nonverbal behavior ofreactions (for example, throwing in an appropriate word) at anappropriate timing in response to the voice information or textinformation serving as input information.

Moreover, in the scene as shown in FIG. 2 above, it is also possible tocreate learning data by acquiring the voice information (X) of thespeaker who is speaking and simultaneously acquiring the nonverbalinformation (Y) of the other participants by using the measuringapparatus.

In this way, if learning of a nonverbal information generation model isperformed on the basis of the nonverbal information of each of aplurality of participants, a plurality of robots and agents can be madeto react at appropriate and different timings to voice information ortext information serving as input information.

In this case, a learned nonverbal information generation model learns acombination of the voice information acquired from the speaker andnonverbal information representing information about the behavior of aninterlocutor of the speaker (for example, a listener of the conversationor a participant of the conversation) as learning data. Here, not only alistener of the conversation and a participant of the conversation butalso observers of the conversation may be included as interlocutors ofthe speaker, and so “entities that exhibit some reaction to thespeaker's voice (and the content thereof)” are also expressed as“listeners to an utterance”.

Further, when targeting text information, a learned nonverbalinformation generation model learns a combination of text informationcorresponding to voice information acquired from the speaker andnonverbal information representing information about the behavior of aninterlocutor of the speaker (for example, the listener of theconversation or a participant of the conversation) as learning data.

It should be noted that the following fourth to sixth embodiments arethe first to third modification examples.

Fourth Embodiment

In the fourth embodiment, with voice information targeted, a nonverbalinformation generation model learning apparatus learns a nonverbalinformation generation model for generating time-information-stampednonverbal information from time-information-stamped voice featurequantities, on the basis of time-information-stamped voice featurequantities for learning extracted from the voice information forlearning output from the speaker (“speaker” in FIG. 2) and nonverbalinformation for learning representing information about the behavior ofan interlocutor of the speaker (for example, “other participants” or“speaker's conversation partner” in FIG. 2). Thereby, a learnednonverbal information generation model representing the behavior of theinterlocutor of the speaker can be obtained in accordance with the voiceinformation acquired from the speaker. It should be noted that, as shownin FIG. 2, “speaker” represents a person who produces voices such as anutterance. Further, the “speaker's conversation partner” represents, forexample, a person who is listening to an utterance or the like utteredby the speaker, and corresponds to an “other participants” and“speaker's conversation partner” shown in FIG. 2. Further, the behaviorof the interlocutor of the speaker is, for example, the reaction of theinterlocutor of the speaker in response to the voice uttered by thespeaker.

Also, when the voice information that is the target of nonverbalinformation generation is input, the nonverbal information generationapparatus generates time-information-stamped nonverbal information onthe basis of the time-information-stamped voice feature quantitiesextracted from the voice information acquired from the speaker and thelearned nonverbal information generation model.

It should be noted that since other configurations and operations of thenonverbal information generation model learning apparatus and thenonverbal information generation apparatus of the fourth embodiment arethe same as those of the first embodiment, descriptions thereof will beomitted.

Fifth Embodiment

In the fifth embodiment, with text information targeted, a nonverbalinformation generation model learning apparatus learns a nonverbalinformation generation model for generating time-information-stampednonverbal information from time-information-stamped text featurequantities, on the basis of time-information-stamped text featurequantities for learning extracted from text corresponding to voiceinformation for learning obtained from a speaker and nonverbalinformation for learning representing information related to thebehavior of the interlocutor of the speaker. Thereby, a learnednonverbal information generation model representing the behavior of theinterlocutor of the speaker is obtained in accordance with the textinformation corresponding to the voice information acquired from thespeaker.

Further, when the text information of the nonverbal informationgeneration target has been input, the nonverbal information generationapparatus generates time-information-stamped nonverbal information onthe basis of the time-information-stamped text feature quantitiesextracted from the text information corresponding to the voiceinformation acquired from the speaker, and the learned nonverbalinformation generation model.

It should be noted that since other configurations and operations of thenonverbal information generation model learning apparatus and thenonverbal information generation apparatus of the fifth embodiment arethe same as those of the second embodiment, descriptions thereof will beomitted.

Sixth Embodiment

In the sixth embodiment, with both voice information and textinformation targeted, a nonverbal information generation model learningapparatus learns a nonverbal information generation model for generatingtime-information-stamped nonverbal information fromtime-information-stamped voice feature quantities andtime-information-stamped text feature quantities, on the basis oftime-information-stamped voice feature quantities for learning,time-information-stamped text feature quantities for learning, andnonverbal information for learning representing information related tothe behavior of the interlocutor of the speaker. Thereby, a learnednonverbal information generation model representing the behavior of theinterlocutor of the speaker is obtained in accordance with the voiceinformation and the text information.

Further, when both voice information and text information of thenonverbal information generation target have been input, the nonverbalinformation generation apparatus generates time-information-stampednonverbal information on the basis of the time-information-stamped voicefeature quantities, the time-information-stamped text featurequantities, and the learned nonverbal information generation model.

It should be noted that since other configurations and operations of thenonverbal information generation model learning apparatus and thenonverbal information generation apparatus of the sixth embodiment arethe same as those of the third embodiment, descriptions thereof will beomitted.

Seventh Embodiment

<Configuration of Nonverbal Information Generation Model LearningApparatus>

Next, a seventh embodiment of the present invention will be described.It should be noted that components with the same configuration as thosein the first embodiment are denoted by the same reference signs, withdescriptions thereof being omitted.

In the seventh embodiment, a gesture corresponding to an input utteranceis generated on the basis of a learned machine learning model. At thattime, as the gesture, not only the presence/absence of behavior but alsoinformation about the magnitude, the number of times, and the ratio ofthe pause length is generated. Moreover, a point of difference from thefirst embodiment is, in addition to uttered voice or text being used asinput, a variable that influences the generation of a gesture is furtherused as “additional information” to perform learning of a nonverbalinformation generation model that generates nonverbal information.

It should be noted that the text information used in the seventhembodiment is text information representing the utterance content when aspeaker is speaking externally via voice.

FIG. 17 is a block diagram showing an example of the configuration of anonverbal information generation model learning apparatus 710 inaccordance with the seventh embodiment. As shown in FIG. 17, thenonverbal information generation model learning apparatus 710 inaccordance with the seventh embodiment is configured by a computerprovided with a CPU, a RAM, and a ROM that stores a program forexecuting a learning processing routine described later. The nonverbalinformation generation model learning apparatus 710 is functionallyprovided with a learning input unit 720 and a learning calculation unit730.

The learning input unit 720 receives learning data including acombination of text information for learning, nonverbal information forlearning, and additional information for learning.

The received nonverbal information includes not only thepresence/absence of behavior but also information about the magnitude,number of times, and ratio of pause length.

Specifically, the nonverbal information includes any behavior includedin the behavior list tables shown in Tables 2 to below.

TABLE 2 Primary Secondary End No. Item Item Content Position 1Head_pitch Initial Normal state (front) Initial 2 nod_1l 1 large nodInitial 3 nod_1m 1 moderate nod Initial 4 nod_1s 1 small nod Initial 5nod_2l 2 large nods Initial 6 nod_2m 2 moderate nods Initial 7 nod_2s 2small nods Initial 8 nod_3l 3 large nods Initial 9 nod_3m 3 moderatenods Initial 10 nod_3s 3 small nods Initial 11 nod_4l 3 large nodsInitial 12 nod_4m 3 moderate nods Initial 13 nod_4s 3 small nods Initial14 nod_5l 5 or more large nods Initial 15 nod_5m 5 or more moderateInitial nods 16 nod_5s 5 or more small nods Initial 17 upper_l Largehead rotation Upper_L upward 18 down_l Large head rotation Down_Ldownward 19 upper_m Moderate head rotation Upper_M upward 20 down_mModerate head rotation Down_M downward 21 upper_s Small head rotationUpper_S upward 22 down_s Small head rotation Down_S downward 24 Head_yawinitial Normal state (front) Initial 25 right_l Large look to rightRight_L 26 left_l Large look to left Left_L 27 right_m Moderate look toright Right_M 28 left_m Moderate look to left Left_M 29 right_s Smalllook to right Right_S 30 left_s Small look to left Left_S 31 shake_lShake head side to side Initial greatly 32 shake_m Shake head side toside Initial moderately 33 shake_s Shake head side to side Initialslightly

TABLE 3 35  Head_roll initial Normal state (front) Initial 36 tilt_right_l Tilt head greatly to right Tilt_right_L 37  tilt_left_lTilt head greatly to left Tilt_left_L 38  tilt_right_m Tilt headmoderately to right Tilt_right_M 39  tilt_left_m Tilt head moderatelygreatly to left Tilt_left_M 40  tilt_right_s Tilt head slightly to rightTilt_right_S 41  tilt_left_s Tilt head slightly to left Tilt_left_S 43 Hand_gesture initial_down Normal state (hands down) Initial_down 44 initial_chest Normal state (hands bent and Initial_chest spread out, atarm height) 46a iconic_1 Express scenic portrayal and actionInitial_chest 46b iconic_2 Express scenic portrayal and actionInitial_chest 46c iconic_6 Express scenic portrayal and actionInitial_chest 47a metaphoric_1 Pictorial, graphic gesture Initial_chest47b metaphoric_2 Pictorial, graphic gesture Initial_chest 47cmetaphoric_7 Pictorial, graphic gesture Initial_chest 48a beat_1 Adjusttone of utterance Initial_chest and emphasize remark 48b beat_2 Adjusttone of utterance Initial_chest and emphasize remark 48c beat_8 Adjusttone of utterance Initial_chest and emphasize remark 49a deictic_1Pointing Initial_chest 49b deictic_2 Pointing Initial_chest 49cdeictic_9 Pointing Initial_chest 50a feedback_1 Sympathize/agreewith/respond to Initial_chest utterance of another person 50b feedback_2Sympathize/agree with/respond to Initial_chest utterance of anotherperson 50c feedback_10 Sympathize/agree with/respond to Initial_chestutterance of another person 51a compellation_1 Call the other personInitial_chest 51b compellation_2 Call the other person Initial_chest 51ccompellation_1_l Call the other person Initial_chest 52a hesitate_1Hesitate to mention Initial_chest 52b hesitate_2 Hesitate to mentionInitial_chest 52c hesitate_12 Hesitate to mention Initial_chest 53aothers_1_1 Touch one's cheeks Others_1_1 53b others_1_2 Touch one'scheeks Others_1_2 53c others_1_13 Touch one's cheeks Others_1_3 54 others_2 Count numbers Others_2 55  others_3 Cross arms Others_3

TABLE 4 56 Facial_expression initial Normal state Initial 57 smile_lLarge smile Smile_L 58 smile_m Medium smile Smile_M 59 smile_s Smallsmile Smile_S 60 anger_l Strong anger Anger_L 61 anger_m Moderate angerAnger_M 62 anger_s Slight anger Anger_S 63 sad_l Strong sadness Sad_L 64sad_m Moderate sadness Sad_M 65 sad_s Slight sadness Sad_S 66 surprise_lStrong surprise Surprise_L 67 surprise_m Moderate surprise Surprise_M 68surprise_s Slight surprise Surprise_S 69 dislike_l Strong dislikeDislike_L 70 dislike_m Moderate dislike Dislike_M 71 dislike_s Slightdislike Dislike_S 72 fear_l Strong fear Fear_L 73 fear_m Moderate fearFear_M 74 fear_s Slight fear Fear_S 75 Upper_body_posture initialUpright (appropriately swaying) Initial 76 forward_l Leaning forwardgreatly Forward_L (appropriately swaying) 77 forward_m Leaning forwardmoderately Forward_M (appropriately swaying) 78 forward_s Leaningforward slightly Forward_S (appropriately swaying) 79 backward_l Leaningforward greatly Backward_L (appropriately swaying) 80 backward_m Leaningforward moderately Backward_M (appropriately swaying) 81 backward_sLeaning forward slightly Backward_S (appropriately swaying) 82 bowBowing Bow

Here, the text information for learning was obtained by manuallytranscribing utterance of each person based on uttered voice data, withthe speech sections being separated by sections of 200 msec or more ofsilence.

Also, the nonverbal information for learning is assigned inconsideration of the movement at the time of utterance corresponding tothe uttered voice data. The nonverbal information includes, for example,a nod, face orientation, hand gesture, gaze, facial expression, bodyposture, body joint, position, movement, size of pupil diameter, andpresence/absence of blinking.

The hand gestures included in the nonverbal information for learningare, for example, annotated while considering the uttered content inaddition to a hand movement during the utterance. Further, the handgestures included in the nonverbal information for learning may beobtained by automatic recognition. With regard to automatic recognitionof hand gestures, many methods using image processing (for example,Reference Document 7) have been proposed, and any method may be used.

-   [Reference Document 7]: Siddharth S. Rautaray and Anupam Agrawal,    “Vision based hand gesture recognition for human computer    interaction: a survey”, Artificial Intelligence Review, January    2015, Volume 43, Issue 1, pp. 1-54.

Here, as the type of hand gesture, the same type of that described inReference Document 8 may be used. For example, (A) Iconic, (B)Metaphoric, (C) Beat, (D) Deictic, (E) Feedback, (F) Compellation, (G)Hesitate, and (H) Others are detailed types of hand gestures.

-   [Reference Document 8]: D. McNeill, “Hand and Mind: What Gestures    Reveal About Thought”, Chicago: University of Chicago Press, 1992.

(A) Iconic is a gesture used to represent scenic portrayal and actions.(B) Metaphoric, like Iconic, is a pictorial and graphic gesture, but thecontents that are instructed are abstract matters and concepts (such asthe passage of time). (C) Beat is a gesture for adjusting the tone of anutterance or emphasizing a remark, and is a gesture of oscillating thehands or waving the hands in response to an utterance. (D) Deictic is agesture that directly points to a direction, a place, or an object suchas pointing. (E) Feedback is a gesture indicating sympathizing,agreement with, or response to the utterance of another person, agesture that accompanies when a person speaks out in response to a priorutterance or gesture of another person, or a gesture of the same shapeimitating the gesture of the other person. (F) Compellation is a gestureto call the other person. (G) Hesitate is a gesture that appears whenone hesitates to mention. (H) Others refers to gestures that seem tohave some meaning but are hard to judge.

It should be noted that, regarding annotations for hand gestures, notonly the type of hand gesture described above but also annotationsindicating the four states of Prep, Hold, Stroke, and Return may beassigned.

Prep indicates a state in which the hand is raised to make a gesturefrom the home position, while Hold indicates a state in which the handis raised in the air (standby time until the gesture starts). Inaddition, Stroke indicates the state of performing a gesture, andprovides annotations of types (A) to (H) above as detailed informationof this state. Return indicates a state in which the hand is returned tothe home position.

Also, with regard to a nod gesture included in the nonverbal informationfor learning, for example, an annotation was performed with respect to anod section in which the head is lowered and returned during anutterance. Moreover, annotations were performed by treating the actionof putting the head forward and back or the action of pulling the headback and then returning it as nodding. The actions of hanging one's heador shaking one's head side to side were not regarded as nodding.

In the case of nodding two or more times consecutively without a pausein between, the continuous sections are combined, with the number oftimes of nodding assigned thereto. The number of times of nodding isclassified by “1 time, 2 times, 3 times, 4 times, and 5 times or more”.

The additional information for learning that is received is additionalinformation for each predetermined processing unit (for example, foreach morpheme). As the additional information, at least one of apersonal attribute, an environment variable, a physical feature, theposture of an action target, the content of dialogue, a humanrelationship, and an emotion is received. Specifically, personalattributes include gender, age, personality, nationality, and culturalsphere, while environmental variables include the number of people in adialogue (one to one, one to many, many to many), temperature,indoor/outdoor, on land/in air/in water, bright/dark, and the like. Inaddition, the physical feature includes three heads tall, clothing (suchas the existence of pockets, the wearing of a skirt, wearing a hat), andfactors that affect action, while the posture of the action targetincludes standing, sitting, and holding something with the hands.Further, the content of the dialogue includes discussion, chatting,explaining, and the like, while the human relationship includes thehuman relationship between the person who generates the gesture and thedialogue partner, such as who has a higher standing and whether there isgoodwill therebetween. The emotion represents internal states includingjoy, anger, sadness, and mental states such as tension/relaxation.

It should be noted that if additional information is information thatdoes not change for each predetermined processing unit (for example,gender), it need not be received for each predetermined processing unit.In this case, upon being received, the additional information may bedeployed in the additional information for each predetermined processingunit on the apparatus side.

The learning calculation unit 730 generates a nonverbal informationgeneration model for generating time-information-stamped nonverbalinformation on the basis of the learning data received by the learninginput unit 720. As shown in FIG. 17, the learning calculation unit 730is provided with the learning information acquisition unit 231, alearning additional information acquisition unit 731, a learning featurequantity extraction unit 732, the nonverbal information acquisition unit33, the generation parameter extraction unit 34, a learning unit 735,and a learned model storage unit 736.

The learning information acquisition unit 231 acquires the voiceinformation for learning corresponding to the text information forlearning and acquires the time information representing the time fromthe start time to the end time when the voice information is emitted.

The learning additional information acquisition unit 731 acquiresadditional information for learning.

The learning feature quantity extraction unit 732 extractstime-information-stamped text feature quantities for learning, whichrepresent the feature quantities of text information for learning, fromthe text information for learning and time information acquired by thelearning information acquisition unit 231. Specifically, the learningfeature quantity extraction unit 732 assigns time information to thetext information for each predetermined analysis unit, and extractstime-information-stamped text feature quantities. FIG. 18 shows anexample of the time-information-stamped text feature quantities. Asshown in FIG. 18, the start time and end time of each morpheme of textinformation are acquired.

Also, the learning feature quantity extraction unit 732 generatestime-information-stamped additional information for learning from theadditional information for learning acquired by the learning additionalinformation acquisition unit 731 and the time information acquired bythe learning information acquisition unit 231. Specifically, thelearning feature quantity extraction unit 732 assigns the timeinformation to the additional information for each predeterminedanalysis unit to generate the time-information-stamped additionalinformation.

FIG. 18 shows an example of time-information-stamped additionalinformation. As shown in FIG. 18, the start time and end time of themorpheme are acquired for the additional information of each morpheme.Further, when there are a plurality of types of additional information(for example, the number of dialogue participants, emotion, andtemperature), the additional information is represented as a vectorarray storing a plurality of types of additional information.

The learning feature quantity extraction unit 732 sets the vector arrayof additional information in the form ofX _(ADD) ^(t) ^(P,s) ^(,t) ^(P,e)   [Expression 32]

It should be noted thatt _(P,s) ,t _(P,e)  [Expression 33]is the start time and end time of the uttered voice corresponding to themorpheme unit.

The learning unit 735 learns a nonverbal information generation modelfor generating time-information-stamped nonverbal information from thetime-information-stamped text feature quantities and the additionalinformation on the basis of the time-information-stamped text featurequantities for learning extracted by the learning feature quantityextraction unit 732, the time-information-stamped additionalinformation, and the time-information-stamped discretized nonverbalinformation for learning extracted by the generation parameterextraction unit 34.

Specifically, the learning unit 735 constructs a nonverbal informationgeneration model that takes the time-information-stamped text featurequantities for learning extracted by the learning feature quantityextraction unit 732X _(D) ^(t) ^(S,s) ^(,t) ^(S,e)   [Expression 34]X _(P) ^(t) ^(P,s) ^(,t) ^(P,e)   [Expression 35]and the time-information-stamped additional information for learningX _(ADD) ^(t) ^(P,s) ^(,t) ^(P,e)   [Expression 36]as inputs, and outputs the nonverbal informationY ^(t) ^(N,s) ^(,t) ^(N,e) .  [Expression 37]

When constructing the nonverbal information generation model, anymachine learning technique may be used, and SVM is used in the presentembodiment. For example, for each type (A) to (H) above, an SVM model iscreated for estimating which action among the actions belonging to eachtype the gesture is. That is, for each type of (A) to (H) above, an SVMmodel is created for estimating which behavior the gesture is among theplurality of actions described as the contents belonging to each type inthe above behavior list tables.

It should be noted that in the nonverbal information generation model,what kind of time resolution is used and which time parameter is used toestimate the nonverbal information are arbitrary. Here is shown anexample of a feature quantity used in the case of estimating a gestureY ^(T1,T2)  [Expression 38]

in an arbitrary time section T1 to T2. The verbal feature quantitiesX _(D) ^(T1,T2)

X _(P) ^(T1,T2)  [Expression 39]the additional informationX _(ADD) ^(T1,T2)  [Expression 40]and the gesture to be outputY ^(T1,T2)  [Expression 41]

obtained in the time between T1 to T2, which is the target ofestimation, are paired, and learning is performed using learning dataincluding a plurality of sets of data of these pairs. The learnednonverbal information generation model becomesM ^(T1,T2)  [Expression 42]

It should be noted that as a setting method of T and T2, for example,when nonverbal information is estimated in morpheme units, the starttime and end time of each morpheme are set to T1 and T2, respectively.In this case, the window width from T2 to T1 differs for each morpheme.

Also,Y ^(T1,T2)  [Expression 43]may be an average value of nonverbal information obtained in T1 to T2, acombination of nonverbal information that has appeared, or a patternthat takes into account the order of appearance. For example, when ahand gesture IDY _(HG) ^(ID)  [Expression 44]isY _(HG) ₁ ^(ID)  [Expression 45]within the section from T1 to T3 (T1<T3<T2) and isY _(HG) ₂ ^(ID)  [Expression 46]within the section from T3 to T2, asY ^(T1,T2),  [Expression 47]the TD having a higher appearance time,

$\begin{matrix}{\left\{ \ {Y_{{{HG}\;}_{1}}^{XD},\ Y_{{{HG}\;}_{2}}^{ID}} \right\},} & \left\lbrack {{Expression}\mspace{14mu} 48} \right\rbrack\end{matrix}$which is combination information, andY _(HG) ₁ ^(ID) −Y _(HG) ₂ ^(ID)  [Expression 49]as an n-gram pattern are adopted.

When using an n-gram pattern, time information of nonverbal informationin the n-gram pattern (in the above example, the respective start timesofY _(HG) ₁ ^(ID) −Y _(HG) ₂ ^(ID)  [Expression 50]is allocated using a predetermined method preset for each nonverbalinformation. However, the time information of the nonverbal informationin this n-gram pattern may also be estimated. In this case, the timeinformation is estimated based on the learning data, using a featurequantity used when estimating the n-gram pattern and the estimatedn-gram.

The learned model storage unit 736 stores the learned nonverbalinformation generation model learned by the learning unit 735. Thelearned nonverbal information generation model generatestime-information-stamped nonverbal information from thetime-information-stamped text feature quantities.

<Configuration of Nonverbal Information Generation Apparatus>

FIG. 19 is a block diagram showing an example of the configuration of anonverbal information generation apparatus 740 in accordance with theseventh embodiment. As illustrated in FIG. 19, the nonverbal informationgeneration apparatus 740 in accordance with the present embodiment isconfigured by a computer provided with a central processing unit (CPU),a random access memory (RAM), and a read only memory (ROM) that stores aprogram for executing a nonverbal information generation processingroutine described later. The nonverbal information generation apparatus740 is functionally provided with an input unit 750, a calculation unit760, and an expression unit 70.

The input unit 750 receives text information and additional information.The additional information to be received is additional information foreach predetermined processing unit (for example, for each morpheme).

The calculation unit 760 is provided with an information acquisitionunit 261, an additional information acquisition unit 761, a featurequantity extraction unit 762, a learned model storage unit 763, and anonverbal information generation unit 764.

The information acquisition unit 261 acquires the text informationreceived by the input unit 750. Moreover, the information acquisitionunit 261 acquires voice information corresponding to the textinformation and acquires time information representing the time from astart time to an end time of the voice information being emitted.

The additional information acquisition unit 761 acquires the additionalinformation received by the input unit 750.

Similarly to the learning feature quantity extraction unit 732, thefeature quantity extraction unit 762 extracts time-information-stampedtext feature quantities representing feature quantities of the textinformation from the text information and the time information acquiredby the information acquisition unit 261. Further, similarly to thelearning feature quantity extraction unit 732, the feature quantityextraction unit 762 generates time-information-stamped additionalinformation from the additional information acquired by the additionalinformation acquisition unit 761 and the time information acquired bythe information acquisition unit 261.

The learned model storage unit 763 stores the same learned nonverbalinformation generation model as the learned nonverbal informationgeneration model stored in the learned model storage unit 736.

The nonverbal information generation unit 764 generatestime-information-stamped nonverbal information corresponding to thetime-information-stamped text feature quantities and additionalinformation extracted by the feature quantity extraction unit 762 on thebasis of the time-information-stamped text feature quantities andtime-information-stamped additional information extracted by the featurequantity extraction unit 762, and the learned nonverbal informationgeneration model stored in the learned model storage unit 763.

For example, the nonverbal information generation unit 764, using thelearned nonverbal information generation model stored in the learnedmodel storage unit 763M ^(T1,T2)  [Expression 51]takes arbitrary feature quantities as the time-information-stamped textfeature quantitiesX _(D) ^(T1,T2)

X _(P) ^(T1,T2)  [Expression 52]and the time-information-stamped additional informationX _(ADD) ^(T1,T2)  [Expression 53]as inputs, and obtains the gestureY ^(T1,T2)  [Expression 54]as a generation parameter corresponding to the time-information-stampednonverbal information.

Then, the nonverbal information generation unit 764 controls theexpression unit 70 such that the time-information-stamped generationparameter that has been generated is output from the expression unit 70on the basis of the time information assigned to the generationparameter.

Specifically, the nonverbal information generation unit 764 causes thegestureY ^(T1,T2)  [Expression 55]to be reflected as an action of an arbitrary target (for example, ananimation character, a robot, or the like) in the expression unit 70.

The expression unit 70 causes the voice information corresponding to thetext information received by the input unit 750 and the nonverbalinformation generated by the nonverbal information generation unit 764to be expressed under the control of the nonverbal informationgeneration unit 764.

<Operation of Nonverbal Information Generation Model Learning Apparatus710>

Next, the Operation of the Nonverbal Information Generation ModelLearning apparatus 710 in accordance with the seventh embodiment will bedescribed. First, when learning data representing a combination of aplurality of pieces of text information for learning, a plurality ofpieces of additional information for learning, and a plurality of piecesof nonverbal information for learning is input to the learning inputunit 720 of the nonverbal information generation model learningapparatus 710, the nonverbal information generation model learningapparatus 710 executes the learning processing routine shown in FIG. 20.

First, in Step S300, the learning information acquisition unit 231acquires the text information for learning from among the plurality ofsets of learning data received by the learning input unit 720(specifically, the pairs of the text information, the additionalinformation, and the nonverbal information).

In Step S102, the nonverbal information acquisition unit 33 acquires,from among the plurality of sets of learning data received by thelearning input unit 720, nonverbal information for learning and timeinformation representing the time from the start time to the end timewhen the behavior represented by the nonverbal information for learningis performed.

In Step S303, the learning text analysis unit 237 performs apredetermined text analysis on the text information for learningacquired in Step S300 and acquires a result of the text analysis.Further, the learning voice synthesis unit 238 synthesizes voiceinformation for learning corresponding to the text information forlearning on the basis of the text analysis result acquired by thelearning text analysis unit 237. Then, the learning voice synthesis unit238 acquires time information indicating the time from the start time tothe end time when the voice information for learning is emitted.

In Step S700, the learning additional information acquisition unit 731acquires the additional information for learning from among theplurality of sets of learning data received by the learning input unit720.

In Step S702, the learning feature quantity extraction unit 732 extractstime-information-stamped text feature quantities for learning from thetext information for learning and the time information acquired in StepS303. Further, the learning feature quantity extraction unit 732generates time-information-stamped additional information for learningfrom the additional information for learning acquired in Step S700 andthe time information acquired in Step S303.

In Step S106, the generation parameter extraction unit 34 extractstime-information-stamped discretized nonverbal information from thenonverbal information for learning and time information acquired in StepS102.

In Step S708, the learning unit 735 learns a nonverbal informationgeneration model for generating a time-information-stamped generationparameter from the time-information-stamped text feature quantities andthe additional information on the basis of the time-information-stampedtext feature quantities for learning and the time-information-stampedadditional information for learning extracted in Step S702 and thetime-information-stamped generation parameter for learning acquired inStep S106.

In Step S110, the learning unit 735 stores the learned nonverbalinformation generation model obtained in Step S708 in the learned modelstorage unit 736, and ends the learning processing routine.

<Operation of Nonverbal Information Generation Apparatus 740>

Next, the operation of the nonverbal information generation apparatus740 in accordance with the seventh embodiment will be described. First,when the learned nonverbal information generation model stored in thelearned model storage unit 736 of the nonverbal information generationmodel learning apparatus 710 is input to the nonverbal informationgeneration apparatus 740, the learned nonverbal information generationmodel is stored in the learned model storage unit 763 of the nonverbalinformation generation apparatus 740. Then, when text information andadditional information that are the target of nonverbal informationgeneration are input to the input unit 750, the nonverbal informationgeneration apparatus 740 executes the nonverbal information generationprocessing routine shown in FIG. 21.

In Step S400, the information acquisition unit 261 acquires the textinformation received by the input unit 750.

In Step S401, the text analysis unit 265 performs a predetermined textanalysis on the text information acquired in Step S400 and acquires aresult of the text analysis. Further, the voice synthesis unit 266synthesizes voice information corresponding to the text information onthe basis of the text analysis result obtained by the text analysis unit265. Then, the voice synthesis unit 266 acquires time informationrepresenting the time from the start time to the end time when the voiceinformation is emitted.

In Step S750, the additional information acquisition unit 761 acquiresthe additional information received by the input unit 750.

In Step S752, the feature quantity extraction unit 762 extractstime-information-stamped text feature quantities from the textinformation and time information acquired in Step S401, and generatestime-information-stamped additional information from the additionalinformation acquired in Step S750 and the time information acquired inStep S401.

In Step S754, the nonverbal information generation unit 764 reads thelearned nonverbal information generation model stored in the learnedmodel storage unit 763.

In Step S756, the nonverbal information generation unit 764 generates atime-information-stamped generation parameter corresponding to thetime-information-stamped text feature quantities and additionalinformation extracted in Step S752, on the basis of thetime-information-stamped text feature quantities and additionalinformation extracted in Step S752, and the learned nonverbalinformation generation model read in Step S754.

In Step S208, the nonverbal information generation unit 764 controls theexpression unit 70 such that the time-information-stamped nonverbalinformation generated in Step S756 is output from the expression unit 70on the basis of the time information assigned to the nonverbalinformation, and ends the nonverbal information generation processingroutine.

It should be noted that since other configurations and operations of thenonverbal information generation apparatus and the nonverbal informationgeneration model learning apparatus in accordance with the seventhembodiment are the same as those in the first embodiment, descriptionsthereof will be omitted.

As described above, the nonverbal information generation apparatus 740in accordance with the seventh embodiment acquires the additionalinformation and generates time-information-stamped nonverbal behaviorcorresponding to the time-information-stamped text feature quantities onthe basis of the time-information-stamped text feature quantities andthe additional information, and the learned model for generatingtime-information-stamped nonverbal information including the number oftimes that behavior is performed or the magnitude of behavior. Thereby,it is possible to automatically associate the text information with thenonverbal information including the number of times that behavior isperformed or the magnitude of behavior, and so the cost of doing so canbe reduced.

Further, by finely setting the type of nonverbal information, includingthe magnitude of behavior and the number of times that the behavior isperformed, more detailed nuances can be represented, and so representingintention with nonverbal information becomes easy. Further, by theassociation of the time information or by combining with the additionalinformation, it is possible to more finely represent changes in anaction due to a difference in the additional information, which makes iteasier to represent emotions, for example.

Further, the nonverbal information generation model learning apparatus710 in accordance with the seventh embodiment learns a nonverbalinformation generation model for generating time-information-stampednonverbal information from time-information-stamped text featurequantities and additional information on the basis oftime-information-stamped text feature quantities for learning andadditional information, and time-information-stamped nonverbalinformation for learning including the number of times that behavior isperformed or the magnitude of behavior. Thereby, it is possible toobtain a nonverbal information generation model for generating nonverbalinformation from text feature quantities while reducing the cost ofassociating text information with nonverbal information including thenumber of times that behavior is performed or the magnitude of behavior.

It should be noted that in the above embodiment, the case in which theadditional information is input has been described as an example, butthe present invention is not limited thereto, and the additionalinformation may be estimated. In this case, as illustrated in FIG. 22, alearning calculation unit 730A of a nonverbal information generationmodel learning apparatus 710A is provided with a learning additionalinformation estimation unit 731A that estimates additional informationfrom the text information included in the learning data received by thelearning input unit 720 and outputs the additional information to thelearning additional information acquisition unit 731. For example, it ispossible to estimate the content of a dialogue and emotions from thetext information.

Moreover, as shown in FIG. 23, a calculation unit 760A of a nonverbalinformation generation apparatus 740A is provided with an additionalinformation estimation unit 761A that estimates the additionalinformation from the text information received by the input unit 750 andoutputs the additional information to the additional informationacquisition unit 761.

When voice information or video information, and not text information,is input, the additional information may be estimated from the voiceinformation or video information. For example, age, gender,environmental variables, physical feature, and the like may be estimatedfrom the video information, while the number of people in a dialogue,emotions, nationalities, indoors/outdoors and the like may be estimatedfrom the voice information.

As mentioned above, when estimating the additional information, timeinformation can also be automatically assigned to the additionalinformation. In addition, when estimating additional information, it ispossible to generate an estimated value of nonverbal information foreach unit at the time of estimation.

Moreover, in the nonverbal information generation apparatus, otheradditional information may be changed in accordance with specificadditional information obtained by estimation. Specifically, otheradditional information may be changed so as to switch to designatedinformation defined in advance for the specific additional information.For example, when it can be estimated from the voice information that aswitch between indoors and outdoors has occurred, other additionalinformation is changed so as to change designated content of clothing,which is a physical feature. Further, the utterance speed of voicesynthesis or the display speed of text may be changed in accordance withemotions. In addition, when a satiety state is detected, the additionalinformation is changed so that the body shape is set to be plump, andwhen the emotion of anger is detected, the additional information ischanged so that the dialogue content is changed to “discussion”.

Further, when emotion labels of predetermined processing units arearranged in the order of “normal” and “anger” in a time series as aresult of the estimation of the additional information and whenoutputting voice at the same time as gestures, the additionalinformation of each predetermined processing unit is passed as aparameter (talk speed) to the voice synthesis unit (not shown). Thereby,for a predetermined processing unit for which the emotion label “anger”has been estimated, the time information of the text information ischanged to be shortened so that a certain number is added to the talkspeed or the talk speed is multiplied by a predetermined amount (FIG.24). It should be noted that in FIG. 24, the horizontal axis with anarrow indicates the time axis.

Also, the time information of the time-information-stamped voice featurequantities of the period in which “anger” has been estimated may bechanged.

In addition, a case in which the nonverbal information includes themagnitude of behavior or the number of times that behavior is performedhas been described as an example, but the present invention is notlimited thereto, and the ratio of the pause length may also be included.

Eighth Embodiment

Next, an eighth embodiment of the present invention will be described.It should be noted that components with the same configuration as thosein the first embodiment are denoted by the same reference signs, withdescriptions thereof being omitted.

Outline of Eighth Embodiment

When the input is text, there are times when detailed time informationof the text cannot be obtained. For example, the case of (1) voicesynthesis (or voice recognition) not having a function of outputtingtime information of a required granularity corresponding to each text,and the case of (2) outputting only text when a gesture is generated (noneed for voice synthesis) and there being no resource for performingvoice synthesis, or the case of voice synthesis processing time notbeing securable (when the input is voice, there is no need to output theoriginal voice information, or there are insufficient resources).

The above case (1) occurs when, for example, deep neural network(DNN)-based voice synthesis is used. Moreover, the case of (2) aboveoccurs due to constraints or the like at the time of actual service.

Therefore, in the present embodiment, when the time information of arequired granularity (for example, a character or a morpheme) cannot beobtained as in (1) above, a voice synthesizer is used to obtain the timeinformation of the required granularity using the voice time length ofeach clause (which is coarser than the required granularity but is theclosest) that can be acquired when generating voice corresponding totext. Specifically, using unit numbers that match the pronunciationcharacteristics of the target language (the number of moras in Japanese,the number of accents in English, etc.), the time information ispartitioned into obtainable units and used as time information of therequired unit.

<Configuration of Nonverbal Information Generation Model LearningApparatus>

As shown in FIG. 25, the learning calculation unit 830 of the nonverbalinformation generation model learning apparatus 810 in accordance withthe eighth embodiment is provided with a learning informationacquisition unit 231, a learning additional information acquisition unit731, a learning feature quantity extraction unit 732, the nonverbalinformation acquisition unit 33, the generation parameter extractionunit 34, a learning data creation unit 833, a learning unit 735, and alearned model storage unit 736.

The learning data creation unit 833 obtains detailed time informationwith regard to the text information for learning acquired by thelearning information acquisition unit 231, and generates detailedtime-information-stamped text information.

Here, the detailed time information is assigned on the basis of theresult of partitioning a range of time when outputting the text forlearning in accordance with the number of partitions when the text hasbeen partitioned into the predetermined units.

For example, when the time information obtained regarding the textinformation is time information in utterance units, and time informationin a predetermined unit (mora unit) should be obtained, the timeinformation is obtained as follows.

First, the utterance length of the text is normalized to 1, and theutterance length in the unit in which the time information can beobtained is partitioned by the number of predetermined units (in thiscase, the number of moras) (see FIG. 26A). Then, the time informationobtained by the partitioning is used as the time information of thepredetermined unit.

Moreover, with respect to the time-information-stamped text featurequantities for learning and time-information-stamped additionalinformation extracted by the learning feature quantity extraction unit732, and the time-information-stamped discretized nonverbal informationfor learning extracted by the generation parameter extraction unit 34,the learning data creation unit 833 uses the detailed time informationobtained above to generate detailed time-information-stamped textfeature quantities for learning, additional information for learning,and nonverbal information for learning.

The learning data creation unit 833 outputs a combination of thedetailed time-information-stamped text feature quantities for learning,the detailed time-information-stamped additional information forlearning, and the detailed time-information-stamped nonverbalinformation for learning as learning data to the learning unit 735.

<Configuration of Nonverbal Information Generation Apparatus>

As shown in FIG. 26B, a calculation unit 860 of a nonverbal informationgeneration apparatus 840 in accordance with the eighth embodiment isprovided with an information acquisition unit 261, an additionalinformation acquisition unit 761, a feature quantity extraction unit762, a learned model storage unit 763, a nonverbal informationgeneration unit 764, and a control unit 870.

Similarly to the first embodiment and the second embodiment, theexpression unit 70, along with outputting the text information,expresses the behavior indicated by the time-information-stampednonverbal information that has been generated in accordance with thetime information. In addition, voice corresponding to the textinformation may also be output.

Similarly to the learning feature quantity extraction unit 732, thefeature quantity extraction unit 762 extracts time-information-stampedtext feature quantities indicating the feature quantities of the textinformation from the text information and time information acquired bythe information acquisition unit 261. Also, similarly to the learningfeature quantity extraction unit 732, the feature quantity extractionunit 762 generates time-information-stamped additional information fromthe additional information acquired by the additional informationacquisition unit 761 and the time information acquired by theinformation acquisition unit 261.

Moreover, similarly to the learning data creation unit 833, the featurequantity extraction unit 762 obtains detailed time information regardingthe text information, and obtains detailed time information of the textinformation.

Further, the feature quantity extraction unit 762 generates detailedtime-information-stamped text feature quantities and additionalinformation using the detailed time information obtained above withregard to the time-information-stamped text feature quantities andadditional information.

The nonverbal information generation unit 764 generates detailedtime-information-stamped nonverbal information corresponding to thedetailed time-information-stamped text feature quantities and additionalinformation extracted by the feature quantity extraction unit 762, onthe basis of the detailed time-information-stamped text featurequantities and the detailed time-information-stamped additionalinformation generated by the feature quantity extraction unit 762, andthe learned nonverbal information generation model stored in the learnedmodel storage unit 763.

Then, the control unit 870 controls the expression unit 70 so that thegeneration parameter corresponding to the detailedtime-information-stamped nonverbal information that has been generatedis output from the expression unit 70, and performs control so that textinformation is output by the expression unit 70 in accordance with thetime information.

Under the control of the control unit 870, the expression unit 70outputs the text information received by the input unit 750 or the voiceinformation corresponding to the text information in accordance with thedetailed time information, and also expresses the nonverbal informationgenerated by the nonverbal information generation unit 764 in accordancewith the detailed time information.

<Operation of Nonverbal Information Generation Model Learning Apparatus810>

Next, the operation of the nonverbal information generation modellearning apparatus 810 in accordance with the eighth embodiment will bedescribed. First, when learning data representing a combination of aplurality of pieces of text information for learning, a plurality ofpieces of additional information for learning, and a plurality of piecesof nonverbal information for learning is input to the learning inputunit 720 of the nonverbal information generation model learningapparatus 810, the nonverbal information generation model learningapparatus 810 executes the learning processing routine shown in FIG. 27.

First, in Step S300, the learning information acquisition unit 231acquires text information for learning from among the plurality of setsof learning data received by the learning input unit 720 (specifically,combinations of text information, nonverbal information, and additionalinformation).

In Step S102, the nonverbal information acquisition unit 33 acquires,from among the plurality of sets of learning data received by thelearning input unit 720, the nonverbal information for learning and timeinformation representing the time from the start time to the end timewhen the behavior represented by the nonverbal information for learningis performed.

In Step S303, the learning text analysis unit 237 performs apredetermined text analysis on the text information for learningacquired in Step S300 and acquires a result of the text analysis. Thelearning voice synthesis unit 238 synthesizes voice information forlearning corresponding to the text information for learning on the basisof the text analysis result acquired by the learning text analysis unit237. Then, the learning voice synthesis unit 238 acquires timeinformation representing the time from the start time to the end timewhen the voice information for learning is emitted.

In Step S700, the learning additional information acquisition unit 731acquires the additional information for learning from among theplurality of sets of learning data received by the learning input unit720.

In Step S702, the learning feature quantity extraction unit 732 extractstime-information-stamped text feature quantities for learning from thetext information for learning and the time information acquired in StepS303. Moreover, the learning feature quantity extraction unit 732generates time-information-stamped additional information for learningfrom the additional information for learning acquired in Step S700 andthe time information acquired in Step S303.

In Step S106, the generation parameter extraction unit 34 extractstime-information-stamped discretized nonverbal information from thenonverbal information for learning and the time information acquired inStep S102.

In Step S800, the learning data creation unit 833 obtains, from the textinformation for learning and the time information acquired in Step S303,detailed time information with regard to the text information forlearning.

Moreover, with regard to the time-information-stamped text featurequantities and additional information extracted by the learning featurequantity extraction unit 732, and the time-information-stampeddiscretized nonverbal information for learning extracted by thegeneration parameter extraction unit 34, the learning data creation unit833 uses the detailed time information obtained above to generatedetailed time-information-stamped text feature quantities for learning,additional information for learning, and nonverbal information forlearning.

The learning data creation unit 833 outputs a combination of thedetailed time-information-stamped text feature quantities for learning,the detailed time-information-stamped additional information forlearning, and the detailed time-information-stamped nonverbalinformation for learning as learning data to the learning unit 735.

In Step S708, the learning unit 735 learns a nonverbal informationgeneration model for generating a detailed time-information-stampedgeneration parameter from the detailed time-information-stamped textfeature quantities and additional information on the basis of thedetailed time-information-stamped text feature quantities for learningand the detailed time-information-stamped additional information forlearning extracted in Step S800 and the detailedtime-information-stamped generation parameter for learning acquired inStep S800.

In Step S110, the learning unit 735 stores the learned nonverbalinformation generation model obtained in Step S708 in the learned modelstorage unit 736, and ends the learning processing routine.

<Operation of Nonverbal Information Generation Apparatus>

Next, the operation of the nonverbal information generation apparatus840 in accordance with the eighth embodiment will be described. First,when the learned nonverbal information generation model stored in thelearned model storage unit 736 of the nonverbal information generationmodel learning apparatus 810 is input to the nonverbal informationgeneration apparatus 840, the learned nonverbal information generationmodel is stored in the learned model storage unit 763 of the nonverbalinformation generation apparatus 840. Then, when text information andadditional information that are targets of nonverbal informationgeneration are input to the input unit 750, the nonverbal informationgeneration apparatus 840 executes the nonverbal information generationprocessing routine shown in FIG. 28.

In Step S400, the information acquisition unit 261 acquires the textinformation received by the input unit 750.

In Step S401, the text analysis unit 265 performs a predetermined textanalysis on the text information acquired in Step S400 and acquires aresult of the text analysis. Further, the voice synthesis unit 266synthesizes voice information corresponding to the text information onthe basis of the text analysis result obtained by the text analysis unit265. Also, the voice synthesis unit 266 acquires time informationrepresenting the time from the start time to the end time when the voiceinformation is emitted.

In Step S750, the additional information acquisition unit 761 acquiresthe additional information received by the input unit 750.

In Step S752, the feature quantity extraction unit 762 extractstime-information-stamped text feature quantities from the textinformation and the time information acquired in Step S401, andgenerates time-information-stamped additional information from theadditional information acquired in Step S750 and the time informationacquired in Step S401.

In Step S850, the feature quantity extraction unit 762 obtains, from thetext information and time information acquired in Step S401, detailedtime information regarding the text information. Moreover, the featurequantity extraction unit 762 generates detailed time-information-stampedtext feature quantities using the detailed time information obtainedabove for the time-information-stamped text feature quantities.

Also, the feature quantity extraction unit 762 generates detailedtime-information-stamped additional information using the detailed timeinformation obtained above with regard to the time-information-stampedadditional information.

In Step S754, the nonverbal information generation unit 764 reads thelearned nonverbal information generation model stored in the learnedmodel storage unit 763.

In Step S756, the nonverbal information generation unit 764 generates atime-information-stamped generation parameter corresponding to thedetailed time-information-stamped text feature quantities and additionalinformation generated in Step S850, on the basis of the detailedtime-information-stamped text feature quantities and additionalinformation generated in Step S850, and the learned nonverbalinformation generation model read in Step S754.

In Step S852, the control unit 870 controls the expression unit 70 suchthat the text acquired in Step S400 and the time-information-stampednonverbal information generated in Step S756 are output from theexpression unit 70 in accordance with the time information, and ends thenonverbal information generation processing routine.

It should be noted that since other configurations and operations of thenonverbal information generation apparatus and the nonverbal informationgeneration model learning apparatus in accordance with the eighthembodiment are the same as those in the first embodiment, descriptionsthereof will be omitted.

As described above, the nonverbal information generation apparatus inaccordance with the eighth embodiment can output text information inaccordance with time information of predetermined units along with theexpression of the behavior indicated by the nonverbal information bypartitioning the time information of text information into timeinformation of predetermined units and assigning to the textinformation, even when time information of the required granularitycannot be obtained for the text information.

It should be noted that in the embodiment described above, the case ofpartitioning the time information using the number of morsas the unitnumber in accordance with the pronunciation characteristics of thetarget language has been described as an example, but the presentinvention is not limited thereto. In the case of English, the timeinformation may be partitioned using the number of accents. Moreover, inaddition to the number of moras and the number of accents, the timeinformation may be partitioned and assigned in accordance with thenumber of parts of speech, the number of categories in a thesaurus, andthe like.

Further, weighting may be performed after the time information has beenpartitioned by a predetermined unit number. The weighting may bedetermined by machine learning, or a weighting DB (in which weighting isset for each of types of units in accordance with the pronunciationcharacteristics of the target language) may be prepared in advance. Thelearning data for machine learning may be created by using a voicesynthesizer that can assign detailed time information or may be createdmanually.

Also, in the nonverbal information generation apparatus, when it issufficient that only text be output, the playback speed information ofthe utterance text is acquired, and the expression of the behavior needonly be synchronized in accordance with the text display of theutterance. Moreover, at that time, it is sufficient that the behavior beexpressed in accordance with the utterance length (or time length of theclause) to be played back, without partitioning the time informationassigned to the nonverbal information by the predetermined unit number.

Ninth Embodiment

Next, a ninth embodiment of the present invention will be described. Itshould be noted that components with the same configuration as those inthe first embodiment are denoted by the same reference signs, withdescriptions thereof being omitted.

Outline of Ninth Embodiment

When insufficient consideration is given to the time-series informationduring the generation of nonverbal information, or when a nonverbalinformation generation model is learned for each of a plurality ofactions to generate nonverbal information individually for each action,unnatural or impossible nonverbal information is generated. Unnaturalnonverbal information leads to behavior that is not appropriate whenperformed at the same time, for example, behavior such as jumping whilebowing. Further, impossible nonverbal information means behavior that isinappropriate when viewed in chronological order, for example, thebehavior of slowly nodding five times assigned to only one morpheme. Bybeing assigned to only one morpheme, this behavior is impossiblebehavior because there is not enough time.

Therefore, in the present embodiment, constraint conditions are set inrelation to the generated nonverbal information, with correction(insertion/deletion/replacement) of the generated data being performed.

For example, in order to express natural nonverbal information,unnatural nonverbal information is deleted from learning data and/orgenerated nonverbal information using constraint conditions.Alternatively, nonverbal information is added using constraintconditions.

At least one of a constraint condition of the nonverbal informationitself, a constraint condition due to the shape of the expression unit(CG character/robot), and a constraint condition using additionalinformation is used as the constraint conditions. Further, theconstraint conditions are manually defined as a set of rules.

Configuration of Nonverbal Information Generation Model LearningApparatus

Since the nonverbal information generation model learning apparatus inaccordance with the ninth embodiment has the same configuration as thenonverbal information generation model learning apparatus 810 inaccordance with the eighth embodiment, the same reference signs aregiven, with descriptions thereof being omitted.

In the nonverbal information generation model learning apparatus inaccordance with the ninth embodiment, with regard totime-information-stamped discretized nonverbal information for learningextracted by the generation parameter extraction unit 34, the learningdata creation unit 833 changes the nonverbal information for learning ortime information so as to satisfy a constraint condition relating to atime series of the nonverbal information, or a constraint conditionrelating to the nonverbal information to which time corresponds.

For example, the minimum necessary time information or number of textunits (clauses, morphemes, etc.) is set as a constraint condition foreach action. For this, constraint conditions are set based on the shapeof the expression unit 70, the possible action speed, the currentposture due to the previous behavior, and the like.

Specifically, the case in which an action is generated for each clausewill be described as an example. In the case of a constraint conditionbeing set in which a hand gesture A always acts across three clauses,when the hand gesture A is generated in only one clause, a change isperformed so as to assign the label of the hand gesture A to thepreceding clause or the subsequent clause or both clauses. If that isnot possible, the behavior label of the hand gesture A is deleted. Itshould be noted that if it is determined that expression of thegenerated nonverbal information is impossible, it is preferable that analternative behavior be prepared.

Also, regarding the setting of constraint conditions, it is possible tocreate in advance, based on actual human behavior data, which behaviorshould be generated and for how long (or the number of text units). Atthis time, as shown in FIG. 2, it is possible to use data created byacquiring nonverbal information (Y) of the speaker who is speaking usinga predetermined measuring apparatus at the same time as acquiring thevoice information (X) of the speaker who is speaking. Thereby, it ispossible to set constraint conditions that enable more natural movementas a human being. It should be noted that if the expression unit 70 doesnot imitate a human being, the constraint conditions may be set to allowunnatural movement.

Also, the learning data creation unit 833, in consideration of thetime-information-stamped additional information extracted by thelearning feature quantity extraction unit 732, changes the nonverbalinformation for learning or time information so as to satisfy constraintconditions set using the additional information. For example, when thetalk speed increases due to emotion, it is conceivable for actions thatwere hitherto possible being no longer performable, and thus the timeinformation assigned to the nonverbal information for learning ischanged.

The learning unit 735 learns a nonverbal information generation modelfor generating time-information-stamped nonverbal information from thetime-information-stamped text feature quantities and additionalinformation on the basis of the time-information-stamped text featurequantities for learning and time-information-stamped additionalinformation extracted by the learning feature quantity extraction unit732, and the time-information-stamped nonverbal information for learningchanged by the learning data creation unit 833. At this time, it ispreferable to learn a nonverbal information generation model usingsequence labeling (conditional random fields (CRF)).

In this case, in order to consider the time series relationship oflabels inY ^(T1,T2)  [Expression 56]sequence information such as Begin, Inside, Outside (BIO) tags may beassigned. For example, when certain labels appear consecutively, the B(Begin) label is assigned to the start label, and the I (Inside) isassigned to subsequent labels. This increases the accuracy whenestimating consecutive labels.

Using the data labeled in this way, a nonverbal information generationmodel for generating gestures is learned by using the technique ofsequence labeling. It is possible to use SVM for this, but it is morepreferable to use hidden Markov model (HMM) or conditional random fields(CRF, see Reference Document 9).

-   [Reference Document 9] Japanese Patent No. 5152918    <Configuration of Nonverbal Information Generation Apparatus>

Since the nonverbal information generation apparatus in accordance withthe ninth embodiment has the same configuration as the nonverbalinformation generation apparatus 740 in accordance with the seventhembodiment, the same reference signs are given, with descriptionsthereof being omitted.

In the nonverbal information generation apparatus in accordance with theninth embodiment, the nonverbal information generation unit 764generates time-information-stamped nonverbal information correspondingto the time-information-stamped text feature quantities and additionalinformation extracted by the feature quantity extraction unit 762 on thebasis of the time-information-stamped text feature quantities andtime-information-stamped additional information generated by the featurequantity extraction unit 762 and the learned nonverbal informationgeneration model stored in the learned model storage unit 763.

Similarly to the learning data creation unit 833, the nonverbalinformation generation unit 764, with regard to the generatedtime-information-stamped nonverbal information, changes the nonverbalinformation or the time information assigned to the nonverbalinformation so as to satisfy a constraint condition relating to the timeseries of the nonverbal information, or a constraint condition relatingto the nonverbal information to which time corresponds.

Then, the nonverbal information generation unit 764 controls theexpression unit 70 so that the generation parameter corresponding to thetime-information-stamped nonverbal information that has been changed isoutput from the expression unit 70 on the basis of the time informationassigned to the generation parameter.

Under the control of the nonverbal information generation unit 764, theexpression unit 70 outputs the text information or the voice informationcorresponding to the text information received by the input unit 750 inaccordance with the detailed time information, and also expresses thenonverbal information generated by the nonverbal information generationunit 764 in accordance with the detailed time information.

<Operation of Nonverbal Information Generation Model Learning Apparatus>

Next, the operation of the nonverbal information generation modellearning apparatus in accordance with the ninth embodiment will bedescribed. First, when learning data representing a combination of aplurality of pieces of text information for learning, a plurality ofpieces of additional information for learning, and a plurality of piecesof nonverbal information for learning is input to the learning inputunit 720 of the nonverbal information generation model learningapparatus, the nonverbal information generation model learning apparatusexecutes the learning processing routine shown in FIG. 29.

First, in Step S300, the learning information acquisition unit 231acquires text information for learning from among the plurality of setsof learning data received by the learning input unit 720 (specifically,combinations of text information, nonverbal information, and additionalinformation).

In Step S102, the nonverbal information acquisition unit 33 acquires,from among the plurality of sets of learning data received by thelearning input unit 720, the nonverbal information for learning and thetime information representing the time from the start time to the endtime when the behavior represented by the nonverbal information forlearning is performed.

In Step S303, the learning text analysis unit 237 performs apredetermined text analysis on the text information for learningacquired in Step S300 and acquires a result of the text analysis.Further, the learning voice synthesis unit 238 synthesizes voiceinformation for learning corresponding to the text information forlearning on the basis of the text analysis result acquired by thelearning text analysis unit 237. Then, the learning voice synthesis unit238 acquires time information representing the time from the start timeto the end time when the voice information for learning is emitted.

In Step S700, the learning additional information acquisition unit 731acquires the additional information for learning from among theplurality of sets of learning data received by the learning input unit720.

In Step S702, the learning feature quantity extraction unit 732 extractsthe time-information-stamped text feature quantities for learning fromthe text information for learning and the time information acquired inStep S303. Moreover, the learning feature quantity extraction unit 732generates time-information-stamped additional information for learningfrom the additional information for learning acquired in Step S700 andthe time information acquired in Step S303.

In Step S106, the generation parameter extraction unit 34 extractstime-information-stamped discretized nonverbal information from thenonverbal information for learning and time information acquired in StepS102.

In Step S900, with regard to the time-information-stamped discretizednonverbal information extracted in Step S106, the learning data creationunit 833 changes the nonverbal information or time information assignedto the nonverbal information so as to satisfy a constraint conditionrelating to the time series of the nonverbal information, or aconstraint condition relating to the nonverbal information to which timecorresponds.

In Step S708, the learning unit 735 learns a nonverbal informationgeneration model for generating a time-information-stamped generationparameter from the time-information-stamped text feature quantities andthe additional information on the basis of the time-information-stampedtext feature quantities for learning and the time-information-stampedadditional information for learning extracted in Step S702 and thetime-information-stamped nonverbal information for learning changed inStep S900.

In Step S110, the learning unit 735 stores the learned nonverbalinformation generation model obtained in Step S708 in the learned modelstorage unit 736, and ends the learning processing routine.

<Operation of Nonverbal Information Generation Apparatus>

Next, the operation of the nonverbal information generation apparatus inaccordance with the ninth embodiment will be described. First, when thelearned nonverbal information generation model stored in the learnedmodel storage unit 736 of the nonverbal information generation modellearning apparatus 810 is input to the nonverbal information generationapparatus, the learned nonverbal information generation model is storedin the learned model storage unit 763 of the nonverbal informationgeneration apparatus. Then, when text information and additionalinformation that are targets of nonverbal information generation areinput to the input unit 750, the nonverbal information generationapparatus executes the nonverbal information generation processingroutine shown in FIG. 30.

In Step S400, the information acquisition unit 261 acquires the textinformation received by the input unit 750.

In Step S401, the text analysis unit 265 performs a predetermined textanalysis on the text information acquired in Step S400 and acquires aresult of the text analysis. Further, the voice synthesis unit 266synthesizes voice information corresponding to the text information onthe basis of the text analysis result obtained by the text analysis unit265. Then, the voice synthesis unit 266 acquires time informationrepresenting the time from the start time to the end time when the voiceinformation is emitted.

In Step S750, the additional information acquisition unit 761 acquiresthe additional information received by the input unit 750.

In Step S752, the feature quantity extraction unit 762 extracts thetime-information-stamped text feature quantities from the textinformation and time information acquired in Step S401, and generatestime-information-stamped additional information from the additionalinformation obtained in Step S750 and the time information obtained inStep S401.

In Step S754, the nonverbal information generation unit 764 reads thelearned nonverbal information generation model stored in the learnedmodel storage unit 763.

In Step S950, the nonverbal information generation unit 764 generates atime-information-stamped generation parameter corresponding to thetime-information-stamped text feature quantities and additionalinformation extracted in Step S752, on the basis of thetime-information-stamped text feature quantities and the additionalinformation extracted in Step S752, and the learned nonverbalinformation generation model read in Step S754. Then, the nonverbalinformation generation unit 764, with regard to thetime-information-stamped generation parameter that has been generated,changes the nonverbal information or the time information assigned tothe nonverbal information so as to satisfy a constraint conditionrelating to the time series of the nonverbal information, or aconstraint condition relating to the nonverbal information to which timecorresponds.

In Step S208, the nonverbal information generation unit 764 controls theexpression unit 70 such that the time-information-stamped nonverbalinformation changed in Step S950 is output from the expression unit 70on the basis of the time information assigned to the nonverbalinformation, and ends the nonverbal information generation processingroutine.

It should be noted that since other configurations and operations of thenonverbal information generation apparatus and the nonverbal informationgeneration model learning apparatus in accordance with the ninthembodiment are the same as those in the first embodiment, descriptionsthereof will be omitted.

As described above, the nonverbal information generation apparatus inaccordance with the ninth embodiment acquires voice informationcorresponding to text information, acquires time informationrepresenting the time from the start time to the end time when the voiceinformation is emitted, generates time-information-stamped nonverbalinformation corresponding to the time-information-stamped text featurequantities on the basis of the time-information-stamped text featurequantities and the learned model for generating time-information-stampednonverbal information, and changes the nonverbal information or timeinformation of the nonverbal information so as to satisfy constraintconditions. This makes it possible to eliminate unnatural nonverbalinformation, automatically associate the text information with thenonverbal information, and reduce the cost of doing so.

Further, the nonverbal information generation model learning apparatusin accordance with the ninth embodiment, with regard totime-information-stamped nonverbal information for learning, changes thenonverbal information or time information of the nonverbal informationso as to satisfy constraint conditions. Then, a nonverbal informationgeneration model for generating time-information-stamped nonverbalinformation from the time-information-stamped text feature quantities islearned on the basis of the time-information-stamped text featurequantities for learning and the time-information-stamped nonverbalinformation for learning. Thereby, it is possible to obtain a nonverbalinformation generation model for generating nonverbal information fromthe text feature quantities while eliminating unnatural nonverbalinformation and reducing the cost of associating text information withnonverbal information. It should be noted that a machine learning modelfor rewriting may be created based on the constraint conditions.

Tenth Embodiment

Next, a tenth embodiment of the present invention will be described. Itshould be noted that components with the same configuration as those inthe first embodiment are denoted by the same reference signs, withdescriptions thereof being omitted.

Outline of Tenth Embodiment

The tenth embodiment differs from the ninth embodiment in that whennonverbal information that is impossible due to constraint conditions isdetected, the time information assigned to the text information ischanged in order to insert a pause into the voice data (includingsynthesized voice) or the display speed of the text (talk speed ofsynthesized voice) is changed so that the constraint conditions aresatisfied.

The present embodiment is effective when nonverbal information is moreimportant than voice (text). In particular, a high advantageous effectcan be expected when creating the voice, which is an output, by voicesynthesis or when outputting text.

<Configuration of Nonverbal Information Generation Model LearningApparatus>

Since the nonverbal information generation model learning apparatus inaccordance with the tenth embodiment has the same configuration as thenonverbal information generation model learning apparatus 810 inaccordance with the eighth embodiment, the same reference signs aregiven, with descriptions thereof being omitted.

In the nonverbal information generation model learning apparatus inaccordance with the tenth embodiment, with regard to text informationfor learning and time-information-stamped text feature quantities forlearning acquired by the learning feature quantity extraction unit 732,the learning data creation unit 833 changes the time information of thetext information for learning and the time information assigned to thetext feature quantities for learning so as to satisfy a constraintcondition relating to the time series of the nonverbal information.

For example, in order to satisfy the constraint condition, the timeinformation assigned to the text information for learning and the textfeature quantities for learning is changed so that a pause is insertedin accordance with the nonverbal information, or the time informationassigned to the text information for learning and the text featurequantities is changed so as to change the display speed of the text(talk speed of synthesized voice) in accordance with the nonverbalinformation.

Moreover, the learning data creation unit 833, in consideration of thetime-information-stamped additional information extracted by thelearning feature quantity extraction unit 732, changes the timeinformation assigned to the text information so as to satisfy aconstraint condition set using the additional information.

The learning unit 735 learns a nonverbal information generation modelfor generating time-information-stamped nonverbal information from thetime-information-stamped text feature quantities and additionalinformation on the basis of the time-information-stamped text featurequantities for learning changed by the learning data creation unit 833,the time-information-stamped additional information extracted by thelearning feature quantity extraction unit 732, and thetime-information-stamped nonverbal information for learning extracted bythe generation parameter extraction unit 34.

<Configuration of Nonverbal Information Generation Apparatus>

Since the nonverbal information generation apparatus in accordance withthe tenth embodiment has the same configuration as the nonverbalinformation generation apparatus 740 in accordance with the seventhembodiment, the same reference signs are given, with descriptionsthereof being omitted.

In the nonverbal information generation apparatus in accordance with thetenth embodiment, the nonverbal information generation unit 764generates time-information-stamped nonverbal information correspondingto the time-information-stamped text feature quantities and additionalinformation extracted by the feature quantity extraction unit 762 on thebasis of time-information-stamped text feature quantities andtime-information-stamped additional information generated by the featurequantity extraction unit 762 and the learned nonverbal informationgeneration model stored in the learned model storage unit 763.

Similarly to the learning data creation unit 833, the nonverbalinformation generation unit 764 changes the time information for thetext information and time-information-stamped text feature quantities soas to satisfy a constraint condition relating to the time series of thenonverbal information.

Then, the nonverbal information generation unit 764 controls theexpression unit 70 so that the generation parameter corresponding to thetime-information-stamped nonverbal information that has been generatedis output from the expression unit 70 on the basis of the timeinformation assigned to the generation parameter.

Under the control of the nonverbal information generation unit 764, theexpression unit 70 outputs the text information or the voice informationcorresponding to the text information received by the input unit 750 inaccordance with the changed time information, and also expresses thenonverbal information generated by the nonverbal information generationunit 764 in accordance with the time information.

It should be noted that since other configurations and operations of thenonverbal information generation apparatus and the nonverbal informationgeneration model learning apparatus in accordance with the tenthembodiment are the same as those in the ninth embodiment, descriptionsthereof will be omitted.

As described above, the nonverbal information generation apparatus inaccordance with the tenth embodiment acquires voice informationcorresponding to text information, acquires time informationrepresenting the time from the start time to the end time when the voiceinformation is emitted, generates time-information-stamped nonverbalinformation corresponding to the time-information-stamped text featurequantities on the basis of the time-information-stamped text featurequantities and the learned model for generating time-information-stampednonverbal information, and changes the time information of the textinformation so as to satisfy constraint conditions. This makes itpossible to eliminate an unnatural one, automatically associate the textinformation with the nonverbal information and to reduce the cost ofdoing so.

In addition, the nonverbal information generation model learningapparatus in accordance with the tenth embodiment, with regard totime-information-stamped text feature quantities for learning andnonverbal information for learning, changes the time information of thetext feature quantities so as to satisfy constraint conditions. Anonverbal information generation model for generatingtime-information-stamped nonverbal information from thetime-information-stamped text feature quantities is learned on the basisof the time-information-stamped text feature quantities for learning andthe time-information-stamped nonverbal information for learning.Thereby, it is possible to obtain a nonverbal information general modelfor generating nonverbal information from the text feature quantitieswhile removing an unnatural one and reducing the cost of associatingtext information with nonverbal information.

Eleventh Embodiment

Next, an eleventh embodiment of the present invention will be described.It should be noted that components with the same configuration as thosein the first embodiment are denoted by the same reference signs, withdescriptions thereof being omitted.

Outline of Eleventh Embodiment

When creating learning data for a gesture scenario and/or a nonverbalinformation generation model, it is indispensable to confirm or correctwhether an action that is appropriate for an utterance, that is, theintended action, is performed. However, it is difficult to understandwhat kind of action is assigned to what kind of utterance whenconfirming or correcting of an action, and so the work cost tends toincrease. Therefore, in the present embodiment, visualizing what kind ofnonverbal information is assigned to what kind of text informationsimplifies confirmation and correction of the action.

Specifically, an easy-to-correct interface is provided by displayingnonverbal information in association with verbal information (text orvoice) that has been partitioned into predetermined units and therebyenabling confirmation of the actual action for each predetermined unit.In addition, learning data can be added/corrected in accordance with thecorrection result, and moreover the nonverbal information generationmodel can be relearned.

Here, the usage scenes of the interface described in the presentembodiment are, for example, the following five scenes.

(1) When creating a gesture scenario, the interface in accordance withthe present embodiment is used. For example, with regard to the textinformation that has been input, time-information-stamped nonverbalinformation is generated on the basis of the learned nonverbalinformation generation model, the generated nonverbal information iscorrected by the user's operation, and the correction result is outputas a fixed scenario.

(2) When modifying learning data, the interface in accordance with thepresent embodiment is used. For example, the input learning data is readin, the text information or the nonverbal information included in thelearning data is corrected by the user's operation, and the correctionresult is output as the learning data.

(3) When adding learning data, the interface in accordance with thepresent embodiment is used. For example, with regard to text informationthat has been input, time-information-stamped nonverbal information isgenerated on the basis of the learned nonverbal information generationmodel, the generated nonverbal information is corrected by the user'soperation, and the correction result is output as learning datacorresponding to the nonverbal information generation model.

(4) When relearning the learned nonverbal information generation model,the interface in accordance with the present embodiment is used. Forexample, with regard to text information that has been input,time-information-stamped nonverbal information is generated on the basisof the learned nonverbal information generation model, the generatednonverbal information is corrected by the user's operation, and thecorrection result is added as learning data corresponding to thenonverbal information generation model to relearn the nonverbalinformation generation model.

(5) The interface in accordance with the present embodiment is used whengenerating the constraint conditions described in the ninth embodimentand the tenth embodiment. For example, the constraint conditions aredefined using the correction result obtained by the same method as anyone of the above (1) to (4).

<Configuration of Nonverbal Information Generation Apparatus>

FIG. 31 is a block diagram illustrating an example of the configurationof a nonverbal information generation apparatus 1140 in accordance withthe eleventh embodiment. As shown in FIG. 31, the nonverbal informationgeneration apparatus 1140 in accordance with the eleventh embodiment isconfigured by a computer provided with a CPU, a RAM, and a ROM thatstores a program for executing a nonverbal information generationprocessing routine described later. The nonverbal information generationapparatus 1140 is functionally provided with an input unit 750, acalculation unit 1160, a display unit 1190, and an output unit 1192.

The input unit 750 receives text information and additional information.The additional information to be received is additional information foreach predetermined processing unit (for example, for each morpheme oreach clause). It should be noted that when the additional informationdoes not change for each predetermined processing unit (for example,gender), it need not be received for each predetermined processing unit.In this case, upon being received, the additional information needs onlybe deployed in the additional information for each predeterminedprocessing unit on the apparatus side.

The calculation unit 1160 is provided with an information acquisitionunit 261, an additional information acquisition unit 761, a featurequantity extraction unit 762, a learned model storage unit 763, anonverbal information generation unit 764, a control unit 1170, alearning data generation unit 1172, and a relearning control unit 1174.It should be noted that the additional information acquisition unit 761may be omitted. In the usage scenes of (1) and (5) above, the learningdata generation unit 1172 and the relearning control unit 1174 may befurther omitted. In addition, in the use scenes of (2) and (3) above,the relearning control unit 1174 may be further omitted.

The information acquisition unit 261 acquires the text informationreceived by the input unit 750 in the same manner as the informationacquisition unit 261 of the nonverbal information generation apparatus240 in accordance with the second embodiment, and additionally acquiresvoice information corresponding to the text information and acquirestime information representing the time from the start time to the endtime when the voice information is emitted.

The additional information acquisition unit 761 acquires the additionalinformation received by the input unit 750, similarly to the additionalinformation acquisition unit 761 of the nonverbal information generationapparatus 740 in accordance with the seventh embodiment.

Similarly to the feature quantity extraction unit 762 of the nonverbalinformation generation apparatus 740 in accordance with the seventhembodiment, the feature quantity extraction unit 762 extractstime-information-stamped text feature quantities representing featurequantities of the text information from the text information and thetime information acquired by the information acquisition unit 261.Further, the feature quantity extraction unit 762 generatestime-information-stamped additional information from the additionalinformation acquired by the additional information acquisition unit 761and the time information acquired by the information acquisition unit261.

Similarly to the learned model storage unit 763 of the nonverbalinformation generation apparatus 740 in accordance with the seventhembodiment, the learned model storage unit 763 stores the same learnednonverbal information generation model as the learned nonverbalinformation generation model stored in the learned model storage unit736.

Similarly to the nonverbal information generation unit 764 of thenonverbal information generation apparatus 740 in accordance with theseventh embodiment, the nonverbal information generation unit 764generates time-information-stamped nonverbal information correspondingto the time-information-stamped text feature quantities and additionalinformation extracted by the feature quantity extraction unit 762 on thebasis of the time-information-stamped text feature quantities and thetime-information-stamped additional information extracted by the featurequantity extraction unit 762, and the learned nonverbal informationgeneration model stored in the learned model storage unit 763.

The control unit 1170 controls the display unit 1190 so as to displaythe time-information-stamped nonverbal information generated by thenonverbal information generation unit 764, and the text information andadditional information received by the input unit 750.

The display unit 1190 is provided with a display screen 1190A and anexpression unit 1190B. It should be noted that in the presentembodiment, the case in which the expression unit 1190B is included inthe display unit 1190 will be described as an example, but the presentinvention is not limited thereto, and the expression unit 1190B may beconfigured with an apparatus (for example, a robot) separate from thedisplay unit 1190.

The expression unit 1190B outputs voice corresponding to the textinformation and expresses behavior indicated by thetime-information-stamped nonverbal information that has been generatedin accordance with the time information. Alternatively, a balloonincluding text information may be displayed.

FIG. 32 shows an example of the display screen 1190A displayed by thedisplay unit 1190 at this time.

In the display screen 1190A, text partitioned into predetermined unitsis displayed, and a label showing the nonverbal information is displayedin association with each predetermined unit of the text on the basis ofthe time information assigned to the text feature quantities and thetime information assigned to the nonverbal information. Moreover, thedisplay screen 1190A may display a voice waveform of the voicecorresponding to the text information.

It should be noted that the assigned time information is the time whenoutputting the text, and similarly to, for example, the eighthembodiment, it may be assigned on the basis of the result ofpartitioning a range of time when outputting the text in accordance withthe number of partitions when the text has been partitioned in thepredetermined units.

In addition, the display unit 1190 includes the expression unit 1190Bthat expresses behavior indicating nonverbal information, and displaysthe display screen 1190A in a state in which instructions for theexpression unit 1190B to start, stop, fast-forward, or rewind by apredetermined unit (for example, one morpheme or one clause) theexpression of behavior can be received. For example, a playback button,a pause button, a rewind button, and a fast-forward button are displayedin the display screen 1190A.

It should be noted that a slide bar capable of receiving instructionsfor fast-forwarding or rewinding the expression of behavior by theexpression unit 1190B may be displayed in the display screen 1190A.

Upon receiving an instruction to start, stop, fast forward, or rewind anexpression, the control unit 1170 controls the expression of thebehavior by the expression unit 1190B in accordance with theinstruction.

Further, the display unit 1190 may perform display in the display screen1190A so that it is possible to identify to which part of the text thebehavior expressed by the expression unit 1190B corresponds. Forexample, a playback bar may be displayed at the corresponding portion inthe text corresponding to the behavior expressed by the expression unit1190B, or the cell color of the corresponding portion in the textcorresponding to the behavior expressed by the expression unit 1190B maybe changed or made to flash.

Further, the display unit 1190 displays the display screen 1190A in astate in which settings of additional information can be received. Uponreceiving a setting of additional information, the control unit 1170outputs the additional information to the feature quantity extractionunit 762, and by further using the additional information, controls thedisplay unit 1190 so as to display in the display screen 1190A a labelshowing the nonverbal information generated by the nonverbal informationgeneration unit 764 and the text.

Further, the display unit 1190 displays the display screen 1190A in astate in which a change instruction for the label indicating thenonverbal information can be received.

Upon receiving a change instruction for the label indicating thenonverbal information, the learning data generation unit 1172 generates,as learning data for learning the nonverbal information generationmodel, a combination of time-information-stamped text feature quantitiesand additional information extracted by the feature quantity extractionunit 762, and a label indicating the nonverbal information changed inaccordance with the change instruction.

Further, the display unit 1190 displays the display screen 1190A in astate in which a relearning instruction of the nonverbal informationgeneration model and a setting of a weight for the learning datagenerated by the learning data generation unit 1172 can be received.Here, the weight for the learning data is set in accordance with howmuch importance is attached to the learning data to be added incomparison with the existing learning data at the time of therelearning. For example, when this weight is set to the maximum value,the nonverbal information generation model is relearned so that Y isalways generated for X of the pair (X, Y) in the added learning data.

Upon receiving the relearning instruction and the setting of the weight,the relearning control unit 1174 uses the learning data generated by thelearning data generation unit 1172 and the set weight to cause thenonverbal information generation model learning apparatus 710 to learnthe nonverbal information generation model.

Specifically, the user operates the display screen 1190A as in thefollowing Step 1 to Step 5.

(Step 1) The text is set by inputting or selecting the text indicatingthe uttered sentence for which nonverbal information is to be generated.For example, text is input when generating a gesture scenario as in theusage scene of (1) above or when adding learning data as in the usagescenes of (3) and (4) above. Further, when modifying the learning dataas in the usage scene of (2) above, a collection of learning data ispresented and the text of the learning data to be modified is selected.

(Step 2) The text indicating the uttered sentence is partitioned intopredetermined units, and the label Y indicating the nonverbalinformation generated for each partitioned unit is displayed.

(Step 3) When the start of expression is instructed, the expression unit1190B is put into operation by the generated nonverbal information.

(Step 4) The user visually confirms the action of the expression unit1190B.

(Step 5) By clicking a cell M (a cell M with a label or a blank cell M)when an odd movement is performed, it is possible to rewrite the labelto show correct nonverbal information. As a result, the label may beadded to the learning data as a label indicating the correct nonverbalinformation for the input utterance (in that case, weighting may also beset).

It should be noted that the time information for each predetermined unitmay be displayed, and the display screen 1190A may be displayed so thatan instruction to change the time information can be received (see FIG.33). For example, by clicking the value of time information, the valuecan be edited, and the value of time information can be changed.Alternatively, the start time for each predetermined unit can be changedby changing the position of the vertical bar indicating the start timefor each predetermined unit to the left or right.

Moreover, a change instruction to insert a pause in the voice data(including synthesized voice) may be displayed in the display screen1190A so that the change instruction can be received. For example, asshown in FIG. 34, it is possible to receive a change instruction toinsert the start position of a pause P (see the dotted vertical line inFIG. 34) in each predetermined unit of text information, and it ispossible to receive a change instruction to change the ratio of thepause length by adjusting the start position of the pause P. Further,with respect to also a pause inserted so as to satisfy the constraintcondition as described in the tenth embodiment, a change instruction forchanging the ratio of the pause length by adjusting the start positionof the pause P may be received.

Further, the text feature quantity for each predetermined unit (forexample, clause) and the generation parameter corresponding to thenonverbal information may be displayed so that a change instruction canbe received. For example, as shown in FIG. 32, when the user aligns themouse cursor with the cell M of the text information or the cell M ofthe nonverbal information and right-clicks, the text feature quantitycorresponding to the cell M is overlay-displayed, whereby a changeinstruction can be received. Further, when the text feature quantitiescorresponding to the cell M of the text information areoverlay-displayed, all the text feature quantities extracted from thetext information of the cell M may be displayed, and when the textfeature quantities corresponding to the cell M of the nonverbalinformation are overlay-displayed, the text feature quantity that is thebasis for generating the nonverbal information may be overlay-displayed.

The nonverbal information generation model learning apparatus inaccordance with the eleventh embodiment is the same as the nonverbalinformation generation model learning apparatus 710 in accordance withthe seventh embodiment, and therefore the same reference signs aregiven, with descriptions thereof being omitted.

<Operation of Nonverbal Information Generation Apparatus>

Next, the operation of the nonverbal information generation apparatus inaccordance with the eleventh embodiment will be described. First, whenthe learned nonverbal information generation model stored in the learnedmodel storage unit 736 of the nonverbal information generation modellearning apparatus 710 is input to the nonverbal information generationapparatus, the learned nonverbal information generation model is storedin the learned model storage unit 763 of the nonverbal informationgeneration apparatus. Then, when text information and additionalinformation that are the target of nonverbal information generation areinput to the input unit 750, the nonverbal information generationapparatus executes the nonverbal information generation processingroutine shown in FIG. 35.

In Step S400, the information acquisition unit 261 acquires the textinformation received by the input unit 750.

In Step S401, the text analysis unit 265 performs a predetermined textanalysis on the text information acquired in Step S400 and acquires aresult of the text analysis. Further, the voice synthesis unit 266synthesizes voice information corresponding to the text information onthe basis of the text analysis result obtained by the text analysis unit265. Then, the voice synthesizing unit 266 acquires time informationrepresenting the time from the start time to the end time when the voiceinformation is emitted.

In Step S750, the additional information acquisition unit 761 acquiresthe additional information received by the input unit 750.

In Step S752, the feature quantity extraction unit 762 extractstime-information-stamped text feature quantities from the textinformation and time information acquired in Step S401, and generatestime-information-stamped additional information from the additionalinformation obtained in Step S750 and the time information obtained inStep S401.

In Step S754, the nonverbal information generation unit 764 reads thelearned nonverbal information generation model stored in the learnedmodel storage unit 763.

In Step S756, the nonverbal information generation unit 764 generates atime-information-stamped generation parameter corresponding totime-information-stamped text feature quantities and additionalinformation extracted in Step S752, on the basis of thetime-information-stamped text feature quantities and additionalinformation extracted in Step S752, and the learned nonverbalinformation generation model read in Step S754.

In Step S1100, the control unit 1170 controls the display unit 1190 soas to display the time-information-stamped nonverbal informationgenerated by the nonverbal information generation unit 764 and the textinformation and additional information received by the input unit 750,and then ends the nonverbal information generation processing routine.

The process of Step S1100 is realized by the processing routine shown inFIG. 36.

First, in Step S1150, the control unit 1170 displays text that ispartitioned into predetermined units, and displays a label indicatingnonverbal information in the display screen 1190A in association witheach predetermined unit of the text on the basis of the time informationassigned to the text feature quantities and the time informationassigned to the nonverbal information.

In Step S1152, the control unit 1170 determines whether or not anoperation on the display screen 1190A has been received. When thecontrol unit 1170 has received an operation on the display screen 1190A,the process proceeds to Step S1154.

In Step S1154, the control unit 1170 determines whether the type ofoperation received in Step S1152 is a change instruction, an expressioninstruction, or a relearning instruction. If the received operation is asetting of additional information or an instruction to change a labelindicating nonverbal information, in Step S1156, the control unit 1170displays in the display screen 1190A a result reflecting the change madein accordance with the change instruction. When the received operationis a setting of additional information, the control unit 1170 furtheroutputs the additional information to the feature quantity extractionunit 762, and displays in the display screen 1190A the label indicatingthe nonverbal information generated by the nonverbal informationgeneration unit 764 and text by further using the additionalinformation.

Moreover, if the received operation is a change instruction for a labelindicating nonverbal information, the learning data generation unit 1172generates a combination of time-information-stamped text featurequantities and additional information extracted by the feature quantityextraction unit 762, and the label indicating the nonverbal informationchanged in accordance with the change instruction as learning data forlearning a nonverbal information generation model, and performs outputto the nonverbal information generation model learning apparatus 710.Then, the process returns to Step S1152.

Further, when an instruction to start, stop, or fast-forward or rewindby one clause at a time the expression of behavior by the expressionunit 1190B is received, in Step S1158, the control unit 1170 controlsthe expression of behavior by the expression unit 1190B in accordancewith the received instruction, and the process returns to Step S1152.

Further, when a relearning instruction and weight setting have beenreceived, in Step S1160, the control unit 1170 uses the learning datagenerated by the learning data generation unit 1172 and the set weightto cause the nonverbal information generation model learning apparatus710 to learn the nonverbal information generation model, and ends theprocessing routine.

As described above, by the user correcting the result of the nonverbalinformation generated using the nonverbal information generation modelfor the input text information and additional information, it ispossible to generate learning data of the nonverbal informationgeneration model and add learning data of the nonverbal informationgeneration model. In addition, by the user instructing relearning of thenonverbal information generation model, the user can update thenonverbal information generation model by performing relearning usingthe added learning data.

As described above, the nonverbal information generation apparatus inaccordance with the eleventh embodiment simplifies the work ofcorrecting nonverbal information by visualizing what kind of nonverbalinformation is assigned to what kind of text information.

It should be noted that a gesture scenario composed oftime-information-stamped text information and nonverbal informationgenerated by the user performing a correction may be output as a fixedscenario.

In addition, similarly to the method described in the ninth embodimentor the tenth embodiment, when rewriting is performed on a combination ofthe time-information-stamped text information and nonverbal informationbased on constraint conditions, the correction may be performed by theuser on the rewriting result, and a machine learning model relating torewriting may be created using that data as learning data.

Further, when rewriting is performed on a combination of thetime-information-stamped text information and nonverbal informationbased on constraint conditions, the rewriting history and whichconstraint condition has been applied may be displayed, and moreover acorrection of the constraint condition itself may be received.

Moreover, the ranking of the text feature quantities associated with thelabel showing the nonverbal information may be obtained from thelearning data and presented to the user. In this case, first, pairs eachcomposed of text feature quantities and a label indicating nonverbalinformation are acquired from the learning data, and for each labelindicating the nonverbal information, the type of the text featurequantity with which the pair is formed and the number of appearancesthereof are counted. Then, for each label indicating the nonverbalinformation, the types of text feature quantities may be rearranged indescending order of the number of the counts to be presented as theranking of the text feature quantities.

Also, after presenting the ranking of the text feature quantities, theselection of the learning data may be received and an edit to theselected learning data may be received. For example, as shown in FIG.32, when the user aligns the mouse cursor with the cell M of thenonverbal information and right-clicks, the ranking of the featurequantity with respect to the label of the cell M is overlay-displayed.Then, a selection instruction (for example, click) of each featurequantity name in the feature quantity ranking can be received. When afeature quantity name is selected, the learning data composed of thenonverbal information label of the cell M and the selected text featurequantity are displayed. At this time, the learning data are displayed soas to be directly editable, with the learning data being edited bydeletion, addition, editing, or the like. Also, when the learning datahas been corrected, the learning data before correction may be added asa negative example.

Moreover, a correction of the voice synthesis parameter (talk speed andthe like) may be displayed so that the correction is received.

It should be noted that in the above seventh to eleventh embodiments,the case in which the input information is text information has beendescribed as an example, but the present invention is not limitedthereto. For example, the input information may be voice information.When the input information is voice information, the learninginformation acquisition unit in the nonverbal information generationmodel learning apparatus is the same as the learning informationacquisition unit 31 of the first embodiment. When the input informationis voice information, the information acquisition unit in the nonverbalinformation generation apparatus is the same as the informationacquisition unit 61 of the first embodiment.

For example, configurations corresponding to combinations of theinformation acquisition unit (or the learning information acquisitionunit) and the feature quantity extraction unit (or the learning featurequantity extraction unit) in each of the above-described embodiments areall four patterns illustrated in FIG. 15. In addition, possiblevariations of combinations of the configurations at the time of learningand at the time of nonverbal information generation are the patternsshown in FIG. 16.

Further, as the learning data used in the nonverbal informationgeneration model learning apparatus described in the fourth to sixthembodiments, for example, in the scene shown in FIG. 2, the acquisitionof the nonverbal information (Y) of a conversation partner who is theinterlocutor of the speaker who is speaking using a measuring apparatusat the same time as the acquisition of the voice information (X) of thespeaker who is speaking may be applied to each of the above seventh toeleventh embodiments.

Experimental Example

Next, an experimental example relating to the fifth embodiment will bedescribed.

[With Respect to Experimental Data]

Corpus data was constructed for two-person dialogues, including textinformation that represents utterances and nonverbal information thatrepresents accompanying nodding. Participants in the two-persondialogues were Japanese men and women in their 20s to 50s who weremeeting for the first time. There were a total of 24 participants (12pairs). The participants sat facing each other. For the dialoguecontent, an animation explanation task was adopted in order to collectabundant data related to nodding that accompanies utterances. After eachparticipant watched different animations, the participant explained thecontents of the animations to the dialogue partner. During a 10-minutedialogue session, one participant explained in detail the animation tothe dialogue partner. The dialogue partner was allowed to freely askquestions to the explainer and talk freely. A directional pin microphoneattached to each subject's chest was used to record utterances. A videocamera was used to record the overall appearance of the dialogue and theappearances of the participants. The video was recorded at 30 Hz. Theacquired text information and nonverbal information are shown below.

Text information representing utterances: After manually transcribingthe uttered words from the voice information, sentences were partitionedfrom the uttered content. Furthermore, each sentence was partitionedinto clauses using a dependency analysis engine (see Reference Documents10 and 11). The number of the partitioned clauses was 11,877.

-   [Reference Document 10] Kenji Imamura, “Japanese Dependency Analysis    of Quasi-Spoken Languages Using Sequence Labeling”, Proceedings of    the 13th Annual Conference of the Linguistic Processing Society, pp.    518-521, 2007.-   [Reference Document 11] E. Charniak, “A Maximum-Entropy-Inspired    Parser”, Proceedings of the 1st North American chapter of the    Association for Computational Linguistics conference, pp. 132-139,    2000.

Nonverbal information representing nodding: Each section in whichnodding occurred in the video was manually labeled. Nodding thatoccurred continuously was treated as a single nodding event.

In manual labeling (annotation), all of the aforementioned data wereintegrated at a 30 Hz time resolution.

[Nonverbal Information Generation Model]

Using the corpus data that was constructed, a nonverbal informationgeneration model was constructed that generates nonverbal informationrepresenting nodding for each clause unit, with words, the respectiveparts of speech and thesaurus items thereof, word positions, anddialogue acts of the entire text information serving as input. In orderto verify whether or not each text information is valid, a nonverbalinformation generation model using each text feature quantity and anonverbal information generation model using all text feature quantitieswere constructed. Specifically, for each clause unit, the decision treealgorithm C4.5 (see Reference Document 12) was used to implement anonverbal information generation model that outputs a binary value forthe presence or absence of nodding, with the text feature quantitiesobtained from the target clause, the clause before the target clause,and the clause after the target clause serving as input. The textfeature quantities used are as follows.

-   [Reference Document 12] J. R. Quinlan, “Improved use of continuous    attributes in c4.5”, Journal of Artificial Intelligence Research, 4:    77-90, 1996.

Character number: Number of characters in a clause

Position: Position of the clause from the beginning or end of thesentence

Word: Word information (bag-of-words) in clauses extracted by themorphological analysis tool Jtag (see Reference Document 13)

Part of speech: Part-of-speech information of a word in the clauseextracted by Jtag

Thesaurus: Thesaurus information of words in a clause based on theJapanese Lexicon (see Reference Document 14)

Dialogue act: Dialogue act (33 types) extracted for each sentence by adialogue act estimation technique using word n-gram and thesaurusinformation (see Reference Documents 4 and 15)

-   [Reference Document 13] Takeshi Fuchi and Shinichiro Takagi,    “Japanese morphological analyzer using word co-occurrence -Jtag-”,    In Proceedings of International conference on Computational    linguistics, pages 409-413, 1998.-   [Reference Document 14] Satoru Ikehara, Masahiro Miyazaki, Satoshi    Shirai, Akio Yokoo, Hiromi Nakaiwa, Kentaro Ogura, Yoshifumi Oyama,    and Yoshihiko Hayashi, “Japanese Lexicon”, iwanami Shoten, 1997.-   [Reference 15] Toyomi Meguro, Ryuichiro Higashinaka, Yasuhiro    Minami, and Kohji Dohsaka, “Controlling listening-oriented dialogue    using partially observable Markov decision processes”, In    Proceedings of International Conference on Computational    Linguistics, pages 761-769, 2010.    [With Respect to Experimental Results]

Out of the data of 24 participants, the data of 23 people was used forlearning, and an evaluation was carried out by the 24 cross-validationmethod in which the data of the remaining 1 person was used forevaluation. Thereby, an evaluation of how much nodding can be generatedfrom the data of others was performed. It should be noted that withregard to the data on the presence or absence of nodding in eachoperation, the number of data items was reduced to match that with thesmall amount of data so that the amount of data was the same. Therefore,the baseline chance level is 0.50. Table 5 shows the average values ofthe performance evaluation results.

As a result of evaluation of the nonverbal information generation model,it was obtained that the accuracy was good in the order of lexicon, partof speech, and word. In machine learning, when the resolution of theextracted text feature quantities is too high (number of types isnumerous), the appearance frequency of each text feature quantity isrelatively low, while text feature quantities that have never appearedeven once in the learning data appear more frequently during execution,which tends to reduce the accuracy of generation. On the other hand, asthe resolution is lowered (the number of types is reduced byabstraction), the above-mentioned problem does not occur, butdifferences in data can no longer be expressed, and so the accuracy ofthe generation tends to decrease.

The thesaurus information consists of words classified by meanings andattributes, and since the number of types thereof is smaller than thenumber of words but more numerous than the parts of speech, it isconsidered that learning was efficiently performed even with thelearning data amount of this experiment. Since the thesaurus informationhas a hierarchical structure and it is possible to perform high-levelconceptualization (abstraction) of words in multiple stages, it is easyto control the degree of abstraction in accordance with the size of thelearning data.

Creating a huge amount of corpus data is expensive and also difficult.When the learning data cannot be sufficiently prepared, a betterlearning effect can be expected by using the thesaurus information evenwith a relatively small amount of data.

TABLE 5 Feature quantity Compatibility rate Reproducibility rate F valueChance level 0.500 0.500 0.500 Number of 0.561 0.556 0.558 charactersWord 0.357 0.529 0.431 Part of speech 0.522 0.528 0.525 Lexicon 0.6150.538 0.579 All 0.578 0.601 0.593

INDUSTRIAL APPLICABILITY

The present invention can be used for, for example, a technique ofexpressing a nonverbal action in accordance with the reproduction ofutterance. In accordance with the present invention, it is possible toautomate the association of at least one of voice information and textinformation with nonverbal information.

DESCRIPTION OF THE REFERENCE SIGNS

-   10, 210, 710, 710A, 810: Nonverbal information generation model    learning apparatus-   20, 220, 720: Learning input unit-   30, 230, 730, 730A, 830: Learning calculation unit-   31, 231, 331, 431: Learning information acquisition unit-   32, 232, 332, 432, 732: Learning feature quantity extraction unit-   33: Nonverbal information acquisition unit-   34: Generation parameter extraction unit-   35, 235, 735: Learning unit-   36, 63, 236, 263, 736, 763: Learned model storage unit-   40, 240, 740, 740A, 840, 1140: Nonverbal information generation    apparatus-   50, 250, 750: Input unit-   60, 260, 760, 760A, 860, 1160: Calculation unit-   61, 261, 361, 461: Information acquisition unit-   62, 262, 362, 462, 762: Feature quantity extraction unit-   64, 264, 764: Nonverbal information generation unit-   70, 1190B: Expression unit-   237, 338, 437: Learning text analysis unit-   238, 438: Learning voice synthesis unit-   265, 366, 465: Text analysis unit-   266, 466: Voice synthesis unit-   337: Learning voice recognition unit-   365: Voice recognition unit-   731: Learning additional information acquisition unit-   731A: Learning additional information estimation unit-   761: Additional information acquisition unit-   761A: Additional information estimation unit-   833: Learning data creation unit-   870, 1170: Control unit-   1172: Learning data generation unit-   1174: Relearning control unit-   1190: Display unit-   1190A: Display screen-   1192: Output unit

The invention claimed is:
 1. A nonverbal information generationapparatus comprising: a hardware processor that generatestime-information-stamped nonverbal information that corresponds totime-information-stamped text feature quantities on the basis of thetime-information-stamped text feature quantities and a learned nonverbalinformation generation model; and an expression device that expressesthe nonverbal information, wherein the time-information-stamped textfeature quantities are configured to comprise feature quantities thathave been extracted from text and time information representing timesassigned to predetermined units of the text, the nonverbal informationis information for controlling the expression device so as to expressbehavior that corresponds to the text, and the hardware processorcontrols the expression device so that the time-information-stampednonverbal information is expressed from the expression device inaccordance with time information assigned to the nonverbal information,and controls the expression device so that the text or voicecorresponding to the text that corresponds to the time information isoutput.
 2. The nonverbal information generation apparatus according toclaim 1, wherein the time information is assigned on the basis of aresult of partitioning a range of time when the text is output inaccordance with the number of partitions when the text has beenpartitioned in the predetermined units.
 3. The nonverbal informationgeneration apparatus according to claim 1, wherein the hardwareprocessor generates the time-information-stamped nonverbal informationthat satisfies a constraint condition relating to time series of thenonverbal information or a constraint condition relating to thenonverbal information corresponding to the same predetermined units. 4.The nonverbal information generation apparatus according to claim 3,wherein the hardware processor changes the time information representingthe times assigned to the predetermined units of the text or the timeinformation of the time-information-stamped nonverbal information so asto satisfy the constraint condition relating to the time series of thenonverbal information or the constraint condition relating to thenonverbal information corresponding to the same predetermined units. 5.A non-transitory computer-readable medium having computer-executableinstructions, that upon execution of the instructions by a processor ofa computer, cause the computer to function as the nonverbal informationgeneration apparatus according to claim
 1. 6. A nonverbal informationgeneration model learning apparatus comprising: a hardware processorthat: acquires text information representing text; extracts, from theacquired text information, time-information-stamped text featurequantities that are configured to comprise feature quantities extractedfrom the text and time information representing times assigned topredetermined units of the text; acquires nonverbal informationrepresenting information relating to behavior of a speaker or behaviorof a listener of speaking of the speaker corresponding to the text whenthe speaker performed the speaking, and time information representingtimes at which the behavior was performed and corresponding to thenonverbal information, and creates time-information-stamped nonverbalinformation; and learns a nonverbal information generation model forgenerating the acquired time-information-stamped nonverbal informationon the basis of the extracted time-information-stamped text featurequantities.
 7. A non-transitory computer-readable medium havingcomputer-executable instructions that, upon execution of theinstructions by a processor of a computer, causes the computer tofunction as the nonverbal information generation model learningapparatus according to claim
 5. 8. A nonverbal information generationmethod comprising: generating time-information-stamped nonverbalinformation that corresponds to time-information-stamped text featurequantities on the basis of the time-information-stamped text featurequantities and a learned nonverbal information generation model; andexpressing the nonverbal information, wherein thetime-information-stamped text feature quantities are configured tocomprise feature quantities that have been extracted from text and timeinformation representing times assigned to predetermined units of thetext, the nonverbal information is information for controllingexpression of the nonverbal information so as to express behavior thatcorresponds to the text, and the hardware processor controls theexpression so that the time-information-stamped nonverbal information isexpressed in accordance with time information assigned to the nonverbalinformation, and controls the expression so that the text or voicecorresponding to the text that corresponds to the time information isoutput.
 9. A nonverbal information generation model learning methodcomprising: acquiring text information representing text; extracting,from the acquired text information, time-information-stamped textfeature quantities that are configured to comprise feature quantitiesextracted from the text and time information representing times assignedto predetermined units of the text; acquiring nonverbal informationrepresenting information relating to behavior of a speaker or behaviorof a listener of speaking of the speaker corresponding to the text whenthe speaker performed the speaking, and time information representingtimes at which the behavior was performed and corresponding to thenonverbal information, and creating time-information-stamped nonverbalinformation; and learning a nonverbal information generation model forgenerating the acquired time-information-stamped nonverbal informationon the basis of the extracted time-information-stamped text featurequantities.